Immerse yourself in vision

Understanding Text Annotation Text annotation, in Artificial Intelligence (particularly in Natural Language Processing),  is the process of labeling or annotating text data so that machine learning models can understand it. Text annotation involves identifying and labeling specific components or features in text data, such as entities, sentiments, or relationships, to train AI models effectively. This process converts raw, unstructured text into structured, machine readable data format. Text Annotation (Source) Types of Text Annotation The types of text annotation vary depending on the specific NLP task. Each type of annotation focuses on a particular aspect of text to structure data for AI models. Following are the main types of text annotation: Named Entity Recognition (NER) In Named Entity Recognition (NER), entities in a text are identified and classified into predefined categories such as people, organizations, locations, dates, and more. NER is used to extract key information from text. It helps understand user-specific entities like name of person, locations or company names etc. Example: In following text data: "Barack Obama was born in Hawaii in 1961."   Following are the text annotations Annotation: "Barack Obama" → PERSON "Hawaii" → LOCATION "1961" → DATE   Sentiment Annotation In Sentiment Annotation text is labeled with emotions or opinions such as positive, negative, or neutral. It may also include fine-grained sentiments like happiness, anger, or frustration. Sentiment analysis is used in applications such as analyzing customer feedback or product reviews, monitoring brand reputation on social media etc. Example: For the following text: "I absolutely love this product; it's amazing!"   The sentiment annotation is following: Sentiment: Positive   Text Classification In text classification, predefined categories or labels are assigned to entire text documents or segments. Text classification is used in applications like spam detection in emails or categorizing news articles by topic (e.g., politics, sports, entertainment). Example: For the following text: "This email offers a great deal on vacations."   The text classification annotation is following: Category: Spam   Part-of-Speech (POS) Tagging In Part-of-Speech tagging, each word in a sentence is annotated with its grammatical role, such as noun, verb, adjective, or adverb. The example applications of parts-of-speech tagging are building grammar correction tools. Example: For the following text: "The dog barked loudly."   The parts-of-speech tagging is following: "The" → DT (Determiner) "dog" → NN (Noun, singular or mass) "barked" → VBD (Verb Past Tense) "loudly" → RB (Adverb)   Coreference Resolution In coreference resolution pronouns or phrases are identified and linked to the entities they refer to within a text. Conference resolutions are used to enhance conversational AI systems to maintain context in dialogue, improving summarization by linking all references to the same entity etc. Example: For the following text: "Sarah picked up her bag and left. She seemed upset."   The annotation would be following: "She" → "Sarah"   Here ‘Sarah” and “She” refers to following: "Sarah" → Antecedent "She" → Anaphor   Dependency Parsing In dependency parsing, the grammatical structure of a sentence is analyzed to establish relationships between "head" words and their dependents. This process results in a dependency tree. In this tree nodes represent words, and directed edges denote dependencies. This illustrates how words are connected to convey meaning. It is used in language translation systems, Text-to-speech applications etc. Example: For the following text: "The boy eats an apple."   The dependency relationships would be following: Root: The main verb "eats" serves as the root of the sentence. Nominal Subject (nsubj): "boy" is the subject performing the action of "eats." Determiner (det): "The" specifies "boy." Direct Object (dobj): "apple" is the object receiving the action of "eats." Determiner (det): "an" specifies "apple."   Semantic Role Labeling (SRL) Semantic Role Labeling (SRL) is a process in Natural Language Processing (NLP) that involves identifying the predicate-argument structures in a sentence to determine "who did what to whom," "when," "where," and "how." By assigning labels to words or phrases, SRL captures the underlying semantic relationships, providing a deeper understanding of the sentence's meaning. Example: In the sentence  "Mary sold the book to John,"    SRL identifies the following components: Predicate: "sold" Agent (Who): "Mary" (the seller) Theme (What): "the book" (the item being sold) Recipient (Whom): "John" (the buyer)   This analysis clarifies that Mary is the one performing the action of selling, the book is the object being sold, and John is the recipient of the book. By assigning these semantic roles, SRL helps in understanding the relationships between entities in a sentence, which is essential for various natural language processing applications. Temporal annotation In Temporal annotation, temporal expressions (such as dates, times, durations, and frequencies) in text are identified. This process enables machines to understand and process time-related information, which is crucial for applications like event sequencing, timeline generation, and temporal reasoning. Key Components of Temporal Annotation: Temporal Expression Recognition: Identifying phrases that denote time, such as "yesterday," "June 5, 2023," or "two weeks ago." Normalization: Converting these expressions into a standard, machine-readable format, often aligning them with a specific calendar date or time. Temporal Relation Identification: Determining the relationships between events and temporal expressions to understand the sequence and timing of events. Example: Consider the sentence: "The conference was held on March 15, 2023, and the next meeting is scheduled for two weeks later."   The temporal annotation would be:   Several standards have been developed to guide temporal annotation: TimeML: A specification language designed to annotate events, temporal expressions, and their relationships in text.  ISO-TimeML: An international standard based on TimeML, providing guidelines for consistent temporal annotation. Intent annotation In Intent annotation, also known as intent classification, the underlying purpose or goal behind a text is identified. This technique enables machines to understand what action a user intends to perform. This is essential for applications like chatbots, virtual assistants, and customer service automation. Example: Consider the user input:  "I need to book a flight to New York next Friday."   The identified Intent is  Intent: "Book Flight"   In this example, the system recognizes that the user's intent is to book a flight which allows the system to proceed with actions related to flight reservations. If you're extracting images and text from PDFs to build a dataset for your multimodal AI model, be sure to explore Encord's Document Annotation Tool—to train and fine-tune high-performing NLP Models and LLMs. The Role of a Text Annotator A text annotator plays an important role in the development, refinement, and maintenance of NLP systems and other text-based machine learning models.  The core responsibility of a text annotator is to enhance raw textual data with structured labels, tags, or metadata that make it understandable and usable by machine learning models. Because machine learning models rely heavily on examples to learn patterns (such as understanding language structure, sentiment, entities, or intent) and must be provided with consistent, high-quality annotations. The work of a  text annotator is to  ensure that these training sets are accurate, consistent, and reflective of the complexities of human language. Key responsibilities includes: Data Labeling: Assigning precise labels to text elements, including identifying named entities (e.g., names of people, organizations, locations) and categorizing documents into specific topics. Content Classification: Organizing documents or text snippets into relevant categories to facilitate structured data analysis. Quality Assurance: Reviewing and validating annotations to ensure consistency and accuracy across datasets. Advanced Text Annotation Techniques Modern generative AI models and associated tools have expanded and streamlined the capabilities of text annotation to great extent. Generative AI models can accelerate and enhance the annotation process and reduce the required manual effort. Following are some advanced text annotation techniques: Zero-Shot and Few-Shot Annotation with Large Language Models Zero-shot and few-shot learning enables text annotators to generate annotations for tasks without requiring thousands of manually labeled examples. Text annotators can provide natural language instructions, examples, or prompts to an LLM to classify text or tag entities based on their pre-training and the guidance given in the prompt. For example, in Zero-shot annotation a text annotator may describe the annotation task and categories (e.g., “Label each sentence as ‘Positive,’ ‘Negative,’ or ‘Neutral’”) LLM. The LLM then annotates text based on its internal understanding. Similarly for Few-shot Annotation, the text annotator provides a few examples of annotated data (e.g., 3-5 sentences with their corresponding labels), and the LLM uses these examples to infer the labeling scheme. It then applies this understanding to new, unseen text. Prompt Engineering for Structured Annotation LLMs respond to natural language instructions. Prompt engineering involves carefully designing the text prompt given to these models to improve the quality, consistency, and relevance of the generated annotations. An instruction template provides the model with a systematic set of instructions describing the annotation schema. For example: “You are an expert text annotator. Classify the following text into one of these categories: {Category A}, {Category B}, {Category C}. If unsure, say {Uncertain}.” Using Generative AI to Assist with Complex Annotation Tasks Some annotation tasks (like relation extraction, event detection, or sentiment analysis with complex nuances) can be challenging. Generative AI can break down these tasks into simpler steps, provide explanations, and highlight text segments that justify certain labels. An LLM can be instructed by text annotators to first identify entities (e.g., people, places, organizations) and then determine relationships between them. The LLM can also summarize larger text before annotation. In this way the annotator focuses on relevant sections and speeding up human-in-the-loop processes. Integration with Annotation Platforms Modern annotation platforms and MLOps tools are integrating generative AI features to assist annotators. For example, they allow an LLM to produce initial annotations, which annotators then refine. Over time, these corrections feed into active learning loops that improve model performance. For example, the active learning and model-assisted workflows in Encord can be adapted for text annotation. By connecting an LLM that provides draft annotations, human annotators can quickly correct mistakes. Those corrections help the model learn and improve. The other tools like Label Studio or Prodigy can include LLM outputs directly into the annotation interface, making the model’s suggestions easy to accept, modify, or reject. Practical Applications of Text Annotation Text annotation can be used in various domains. Following are some examples of text annotation to enhance applications, improve data understanding, and provide better end-user experiences. Healthcare The healthcare industry generates vast amounts of text data every day consisting of patient records, physician notes, pathology reports, clinical trial documentation, insurance claims, and medical literature. However, these documents are often unstructured, making it difficult to use them for analytics, research, or clinical decision support. Text annotation makes this unstructured data more accessible and useful. Following are some examples: In Electronic Health Record (EHR) analysis medical entities such as symptoms, diagnoses, medications, dosages, and treatment plans in a patient’s EHR are identified and annotated. Once annotated, these datasets enable algorithms to automatically extract critical patient information.  A model might highlight that a patient with diabetes (diagnosis) is taking metformin (medication) and currently experiences fatigue (symptom). This helps physicians quickly review patient histories, ensure treatment adherence, and detect patterns that may influence treatment decisions. E-Commerce E-commerce platforms handle large amounts of customer data such as product descriptions, user-generated reviews, Q&A sections, support tickets, chat logs, and social media mentions. Text annotation helps structure this data, enabling advanced search, personalized recommendations, better inventory management, and improved customer service. For example, in product categorization and tagging the product titles and descriptions with categories, brands, material, style, or size etc. are annotated. Annotated product information allows recommendation systems to group similar items and suggest complementary products. For instance, if a product is tagged as “women’s sports shoes,” the recommendation engine can show running socks or athletic apparel. This enhances product discovery, making it easier for customers to find what they’re looking for, ultimately increasing sales and customer satisfaction. Sentiment Analysis Sentiment analysis focuses on determining the emotional tone of text. Online reviews, social media posts, comments, and feedback forms contain valuable insights into customer feelings, brand perception, and emerging trends. Annotating this text with sentiment labels (positive, negative, neutral) enables models to gauge public opinion at scale. For example, in brand reputation management user tweets, blog comments, and forum posts are annotated as positive, negative, or neutral toward the brand or a product line. By analyzing aggregated sentiment over time, companies can detect negative spikes that indicate PR issues or product defects. They can then take rapid corrective measures, such as addressing a manufacturing flaw or releasing a statement. It helps maintain a positive brand image, guides marketing strategies, and improves customer trust. Enhancing Text Data Quality with Encord Encord offers a comprehensive document annotation tool designed to streamline the text annotation for training LLM. Key features include: Text Classification This feature allows users to assign predefined categories to entire documents or specific text segments, ensuring that data is systematically organized for analysis. Text Classification (Source) Named Entity Recognition (NER) This feature of Encord enables the identification and labeling of entities such as names, organizations, dates, and locations within the text, facilitating structured data extraction. Named Entity Recognition Annotation (Source) Sentiment Analysis This feature assesses and annotates the sentiment expressed in text passages, helping models understand the emotional context. Sentiment Analysis Annotation (Source) Question Answering This feature helps annotate text to train models capable of responding accurately to queries based on the provided information. QA Annotation (Source) Translation Under this feature, a free-text field enables labeling and translation of text. It supports multilingual data processing. Text Translation (Source) To accelerate the annotation process, Encord integrates state-of-the-art models such as GPT-4o and Gemini Pro 1.5 into data workflows. This integration allows for auto-labeling or pre-classification of text content, reducing manual effort and enhancing efficiency.  Encord's platform also enables the centralization, exploration, and organization of large document datasets. Users can upload extensive collections of documents, apply granular filtering by metadata and data attributes, and perform embeddings-based and natural language searches to curate data effectively.  By providing these robust annotation capabilities, Encord assists teams in creating high-quality datasets, thereby boosting model performance for NLP and LLM applications. Key Takeaways This article highlights the essential insights from text annotation techniques and their significance in natural language processing (NLP) applications: The quality of annotated data directly impacts the effectiveness of machine learning models. High-quality text annotation ensures models learn accurate patterns and relationships, improving overall performance. Establishing precise rules and frameworks for annotation ensures consistency across annotators. Annotation tools like Labelbox, Prodigy, or Encord streamline the annotation workflow. Generative AI models streamline advanced text annotation with zero-shot learning, prompt engineering, and platform integration, reducing manual effort and enhancing efficiency. Encord improves text annotation by integrating model-assisted workflows, enabling efficient annotation with active learning, collaboration tools, and scalable AI-powered automation.

With the world moving towards audio content, speaker recognition has become essential for applications like audio transcription, voice assistants, and personalized audio experiences. Accurate speaker recognition improves user engagement. This guide provides an overview about speaker recognition, how it works,  the challenges of annotating speech files, and how audio management tools like Encord simplify these tasks.   What is Speaker Recognition? Speaker recognition is the process of identifying or verifying a speaker using their voice. Unlike speech recognition, which focuses on transcribing the spoken words, speaker recognition focuses on who the speaker is. The unique characteristics of a person’s speech, such as pitch, tone, and speaking style are used to identify each speaker.  Overview of a representative deep learning-based speaker recognition framework. (Source: MDPI) How Speaker Recognition Works The steps involved in speaker recognition are: Step 1: Feature Extraction The audio recordings are processed to extract features like pitch, tone, and cadence. These features help distinguish between different speakers based on the unique qualities of human speech. Step 2: Preprocessing This step involves removing background noise and normalizing audio content to ensure the features are clear and consistent. This is especially important for real-time systems or while operating in noisy environments. Step 3: Training Machine learning models are trained on a dataset of known speakers’ voiceprints. The training process involves learning the relationships between the extracted features and the speaker’s identity. For more information on audio annotation tools, read the blog Top 9 Audio Annotation Tools. Types of Speaker Recognition Projects There are several variations of the artificial intelligence models, each suited to specific use cases. Speaker Identification: This is used to identify an unknown speaker from a set of speakers. It is commonly used in surveillance, forensic analysis, and in systems where access needs to be granted based on the speaker's identity. Speaker Verification: This confirms the identity of a speaker like voice biometrics for banking or phone authentication. It compares a user’s voice to a pre-registered voice command to authenticate access. Text-Dependent vs. Text-Independent: Voice recognition can also be categorized based on the type of speech involved. Text-dependent systems require the speaker to say a predefined phrase or set of words, while text-independent systems allow the speaker to say any sentence. Text-independent systems are more versatile but tend to be more complex. Real-World Applications of Speaker Recognition Security and Biometric Authentication Speaker recognition is used for voice-based authentication systems, such as those in banking or mobile applications. It allows for secure access to sensitive information based on voiceprints. Forensic Applications Law enforcement agencies use speaker recognition to identify individuals in audio recordings, such as those from criminal investigations or surveillance. Customer Service Speaker recognition is integrated into virtual assistants, like Amazon’s Alexa or Google Assistant, as well as customer service systems in call centers. This allows for voice-based authentication and personalized service. For more information on application of AI audio models, read the blog Exploring Audio AI: From Sound Recognition to Intelligent Audio Editing Challenges in Speaker Recognition Variability in Voice A speaker’s voice can change over time due to illness, aging, or emotional state. This change can make it harder for machine learning models to accurately recognize or verify a speaker’s identity. Environmental Factors Background noise or poor audio recording conditions can distort speech, making it difficult for speaker recognition systems to correctly process audio data. Systems must be robust enough to handle such variations, particularly for real-time applications. Data Privacy and Security The use of speaker recognition raises concerns about the privacy and security of voice data. If not properly protected, sensitive audio recordings could be intercepted or misused. Cross-Language and Accent Issues Speaker recognition systems may struggle with accents or dialects. A model trained on a particular accent may not perform well on speakers with a different one. The ML models need to be trained on a well curated dataset to account for such variations. Importance of Audio Data Annotation for Speaker Recognition Precise labeling and categorization of audio files are critical for machine learning models to accurately identify and differentiate between speakers. By marking specific features like speaker transitions, overlapping speech, and acoustic events, annotated datasets provide the foundation for robust feature extraction and model training. For instance, annotated data ensures that voiceprints are correctly matched to their respective speakers. This is crucial for applications like personalized voice assistants or secure authentication systems, where even minor inaccuracies could compromise user experience or security. Furthermore, high-quality annotations help mitigate biases, improve system performance in real-world conditions, and facilitate advancements in areas like multi-speaker environments or noisy audio recognition. Challenges of Annotating Speech Files Data annotation is important in training AI models for speaker recognition, just like any other application. Annotating audio files with speaker labels can be time consuming and prone to error, especially with large datasets. Here are some of the challenges faced when annotating speech files: Multiple Speakers In many audio recordings, there may be more than one speaker. Annotators must accurately segment the audio into different speakers, a process known as speaker diarization. This is challenging in cases where speakers talk over each other or where the audio is noisy. Background Noise  Annotating speech in noisy environments can be difficult. Background noise may interfere with the clarity of spoken words, requiring more effort to identify and transcribe the speech accurately. Consistency and Quality Control Maintaining consistency in annotations is crucial for training accurate machine learning models. Discrepancies in data labeling can lead to poorly trained models that perform suboptimally. Therefore, validation and quality control steps are necessary during the data annotation process. Volume of Data Annotating large datasets of audio content can be overwhelming. For effective training of machine learning models, large amounts of annotated audio data are necessary, making the annotation process a bottleneck. Speaker Recognition Datasets Using high-quality publicly available annotated datasets can be the first step of your speaker recognition project. This will help in providing a solid foundation for research and development. Here are some of the open-source datasets curated for building speaker recognition models: VoxCeleb: A large-scale dataset containing audio recordings of over 7,000 speakers collected from interviews, YouTube videos, and other online sources. It includes diverse speakers with various accents and languages, making it suitable for speaker identification and verification tasks. LibriSpeech: A set of almost 1,000 hours of English speech collected from audiobooks. While primarily used for automatic speech recognition (ASR) tasks, it can also support speaker recognition through its annotated speaker labels. Common Voice by Mozilla: A crowdsourced dataset with audio clips contributed by users worldwide. It covers a wide range of languages and accents, making it a valuable resource for training multilingual speaker recognition systems. AMI Meeting Corpus: This dataset focuses on meeting scenarios, featuring multi-speaker audio recordings. It includes annotations for speaker diarization and conversational analysis, useful for systems requiring speaker interaction data. TIMIT Acoustic-Phonetic Corpus: A smaller dataset with recordings from speakers across various regions in the U.S. It is often used for benchmarking speaker recognition and speech processing algorithms. Open datasets are a great start, but for specific projects, you’ll need custom annotations. That’s where tools like Encord’s audio annotation platform come in, making it easier to label audio accurately and efficiently. Using Encord’s Audio Annotation Tool Encord is a comprehensive multimodal AI data platform that enables the efficient management, curation and annotation of large-scale unstructured datasets including audio files, videos, images, text, documents and more.  Encord’s audio annotation tool is designed to curate and manage audio data for specific use cases, such as speaker recognition. Encord supports a number of audio annotation use cases such as speech recognition, emotion detection, sound event detection and whole audio file classification. Teams can also undertake multimodal annotation such as analyzing and labeling text and images alongside audio files. Encord Key Features Flexible Classification: Allows for precise classification of multiple attributes within a single audio file down to the millisecond. Overlapping Annotations: Supports layered annotations, enabling the labeling of multiple sound events or speakers simultaneously. Collaboration Tools: Facilitates team collaboration with features like real-time progress tracking, change logs, and review workflows. Efficient Editing: Provides tools for revising annotations based on specific time ranges or classification types. AI-Assisted Annotation: Integrates AI-driven tools to assist with pre-labeling and quality control, improving the speed and accuracy of annotations. Audio Features  Speaker Diarization: Encord’s tools facilitate the segmentation of audio files into audio segments for each speaker, even in cases of overlapping speech. This improves the accuracy of speaker identification and verification. Noise Handling: The platform helps annotators distinguish speech from background noise, ensuring cleaner annotations and improving the overall quality of the training data. Collaboration and Workflow: Encord allows multiple annotators to work together on large annotation projects. It supports quality control measures to ensure that the annotations are consistent and meet the required standards. Data Inspection with Metrics and Custom Metadata: With over 40 data metrics and custom data, Encord makes it easier to get more granular insights into your data. Scalability: The annotation workflow can be scaled to handle large datasets, making sure that machine learning models are trained with high-quality annotated audio data. Strength The platform’s support for complex, multilayered annotations, real-time collaboration, and AI-driven annotation automation, along with its ability to handle various file types like WAV and an intuitive UI with precise timestamps, makes Encord a flexible, scalable solution for AI teams of all sizes preparing audio data for AI model development. Best Practices for Annotating Audio for Speaker Recognition Segment Audio by Speaker Divide audio recordings into precise segments where speaker changes occur. This is necessary for speaker diarization and for ensuring ML models can differentiate between speakers. Reduce Background Noise Preprocess the audio files to remove background noise using filtering techniques. Clean audio improves the accuracy of speaker labels and ensures that algorithms focus on speaker characteristics rather than environmental interference. Make sure not to remove too much of the noise, otherwise the model may not perform well in real-world applications. Handle Overlapping Speech In conversational or meeting audio, where interruptions or crosstalk are frequent, it is important to annotate overlapping speech. This can be done by tagging simultaneous audio segments with multiple labels.  Use Precise Timestamps The proper alignment of audio and transcription can be ensured with accurate timestamping. Hence, each spoken segment should be annotated. Automate Where Possible Integrate semi-automated approaches like speech-to-text APIs (e.g., Google Speech-to-Text, AWS Transcribe) or speaker diarization models to reduce manual annotation workload. These methods can quickly identify audio segments and generate preliminary labels, which can then be fine-tuned by annotators. Open-Source Models for Speaker Recognition Projects Here are some of the open-source models to provide a solid foundation to get started with your speaker recognition project: Whisper by OpenAI Whisper is an open-source model trained on a large multilingual and multitasking dataset. While primarily known for its accuracy in speech-to-text and translation tasks, Whisper can be adapted for speaker recognition when paired with speaker diarization techniques. Its strengths lie in handling noisy environments and multilingual data. DeepSpeech by Mozilla DeepSpeech is a speech-to-text engine inspired by Baidu’s Deep Speech research. It uses deep neural networks to process audio data and offers ease of use with Python. While it focuses on speech-to-text, it can be extended for speaker recognition by integrating diarization models.  Kaldi Kaldi is a speech recognition toolkit widely used for research and production. It includes robust tools for speaker recognition, such as speaker diarization capabilities. Kaldi’s use of Hidden Markov Models (HMMs) and Gaussian Mixture Models (GMMs) provides a traditional yet effective approach to speech processing. SpeechBrain SpeechBrain is an open-source PyTorch-based toolkit that supports multiple speech processing tasks, including speaker recognition and speaker diarization. It integrates easily with Hugging Face, making pre-trained models easily accessible. Its modular design makes it flexible for customization.  Choosing the Right Model Each of these models has its strengths—some excel in ease of use, others in language support or resource efficiency. Depending on your project’s requirements, you can use one or combine multiple models. Make sure to factor in preprocessing steps like separating overlapping audio segments or cleaning background noise, as some tools may require additional input data. These tools will help streamline your workflow, providing a practical starting point for building your speaker recognition pipeline. Key Takeaways: Speaker Recognition Speaker recognition identifies or verifies a speaker based on unique voice characteristics. Applications include biometric authentication, forensic analysis, and personalized virtual assistants. Difficulties like handling overlapping speech, noisy recordings, and diverse accents can hinder accurate annotations. Proper segmentation and consistent labeling are critical to ensure the success of speaker recognition models. High-quality audio annotation is crucial for creating robust speaker recognition datasets. Annotating features like speaker transitions and acoustic events enhances model training and real-world performance. Segmenting audio, managing overlapping speech, and using precise timestamps ensure high-quality datasets. Automation tools can reduce manual effort, accelerating project timelines. Audio annotation projects can be tricky, with challenges like overlapping speech and background noise, but using the right tool can make a big difference. Encord’s platform helps speed up the annotation process and keeps things consistent, which is key for training reliable models. As speaker recognition technology advances, having the right resources in place will help you get better results faster. Consolidate and scale audio data management, curation and annotation workflows on one platform with Encord’s Audio Annotation Tool.

In 2024, we have seen a clear trend of moving from AI chatbots to advanced agentic AI systems. Unlike traditional AI models that perform predefined tasks, agentic AI systems possess autonomy, enabling them to make decisions and execute actions to achieve specific goals with minimal human intervention.  This evolution has the potential to transform industries by automating complex workflows and enhancing decision-making processes. Agentic AI refers to artificial intelligence systems with autonomy, decision-making capabilities, and adaptability. These systems are designed to pursue complex objectives and manage tasks with limited direct human supervision, allowing them to interpret nuanced contexts and make informed decisions. The importance of agentic AI lies in its ability to automate intricate workflows and improve decision-making across various sectors. By operating independently, these systems can handle tasks ranging from customer service automation to managing financial portfolios, thereby increasing efficiency and reducing operational costs.  In this article, we will explore the architecture of agentic AI systems, examine the frameworks that support their development, and discuss practical implementations across various industries. By understanding these aspects, readers will gain insight into how agentic AI is reshaping the technological landscape and driving innovation. Automate your data pipelines with Encord Agents. Get a Free Trial. Understanding Agentic AI Architectures What Are Agentic AI Systems? Agentic AI refers to artificial intelligence systems designed to act autonomously, capable of performing tasks, making decisions, and interacting with their environments without requiring direct human intervention.  These systems are engineered to pursue goals independently, leveraging advanced algorithms and sensory inputs to execute real-time actions. They can learn and optimize their performance through continuous feedback, making them highly adaptive and efficient in dynamic environments. Agentic AI systems differ from traditional AI in several key ways: Autonomy: They can function without constant human oversight, making them ideal for scenarios where human intervention is impractical or unnecessary. Flexibility: These systems can adapt to new data and circumstances, handling unexpected inputs and changes in their environment without manual oversight. Problem-solving: With advanced reasoning, planning, and goal-setting abilities, agentic AI can tackle complex, multi-step problems, often beyond the capabilities of traditional AI. Creativity: Agentic AI can explore novel solutions and hypotheses, potentially leading to breakthroughs in various fields, including drug discovery and precision medicine. Core Components of Agentic AI Architectures Agentic AI systems can be structured around four integral components: Perception, Decision-Making, Learning, and Action. Each module plays a pivotal role in enabling autonomous operation and adaptability. Figure: AI Agent Core Components (Source) Perception Module The Perception module is responsible for collecting and interpreting data from the environment. This involves utilizing sensor technologies and data ingestion pipelines to capture diverse inputs.  For instance, in autonomous driving, systems employ cameras, LiDAR, and radar to gather visual and spatial information, which is then processed to understand the vehicle's surroundings. Recent advancements propose integrating perception and decision-making processes using Transformer architectures to handle multimodal data efficiently. Decision-Making Engine The Decision-Making Engine employs algorithms and models to guide actions and assess potential risks. Reinforcement learning is a prominent approach where agents learn optimal behaviors through trial and error, receiving feedback from their actions.  Heuristic-based decision trees offer structured pathways for decision-making by applying predefined rules. Emerging research emphasizes the importance of robust reasoning and planning capabilities in AI agents to achieve complex goals. Learning Mechanism The Learning Mechanism enables agents to adapt over time through various machine-learning techniques: Supervised Learning: Training on labeled data to recognize patterns and make accurate predictions. Unsupervised Learning: Identifying hidden structures in unlabeled data, facilitating the discovery of underlying patterns. Reinforcement Learning: Learning optimal actions by interacting with the environment and receiving feedback through rewards or penalties. Integrating these learning paradigms allows agents to refine their behaviors and improve performance across diverse tasks. Action Module The Action Module executes decisions and interfaces with real-world or simulated environments. In robotics, this involves controlling actuators to perform physical tasks. In digital systems, it may entail initiating processes or communicating with other software components. The effectiveness of this module depends on the precision and timeliness of actions, ensuring that decisions lead to desired outcomes. Single-Agent vs. Multi-Agent Systems Agentic systems are often categorized as single-agent or multi-agent, each with distinct characteristics and applications. Single-agent systems involve one autonomous entity operating independently to achieve specific objectives. These systems are designed to perform tasks without interacting with other agents. For example, a personal assistant application that manages a user's schedule operates as a single-agent system, focusing solely on its designated tasks. Multi-agent systems (MAS) consist of multiple interacting agents collaborating or competing within a shared environment. These agents can communicate, coordinate, and negotiate to achieve individual or collective goals.  MAS can be effective in complex scenarios where tasks can be distributed among agents, enhancing efficiency and scalability. For instance, in swarm robotics, multiple robots work together to accomplish tasks like search and rescue operations, leveraging their collective capabilities. Figure: Single-agent vs Multi-Agent Systems (Source) Use Cases Single-agent systems are ideal for self-contained tasks that do not require interaction with other entities. Examples include automated data entry tools and standalone diagnostic systems. Multi-agent systems excel in environments requiring coordination among multiple entities. Applications include distributed problem-solving, traffic management systems where multiple agents (e.g., traffic lights, vehicles) coordinate to optimize flow, and collaborative filtering in recommendation systems. Recent research has advanced the development of MAS frameworks. For example, AgentScope is a flexible platform that facilitates robust multi-agent applications by providing built-in agents and service functions, lowering development barriers, and enhancing fault tolerance. While single-agent systems are more straightforward to design and implement, they may need help with complex tasks requiring diverse expertise. Conversely, MAS can handle such tasks more effectively but introduce challenges in coordination and communication among agents.  Understanding the distinctions between these systems is crucial for selecting the appropriate approach based on specific application requirements. Popular Frameworks for Building AI Agents Overview of Leading Frameworks Several frameworks have emerged to facilitate the development of AI agents, each offering unique features and capabilities. Here's an overview of some leading frameworks:   Table 1: Comparison of different Agentic AI frameworks AutoGen AutoGen is an open-source framework developed by Microsoft for building AI agent systems. It simplifies the creation of event-driven, distributed, scalable, and resilient agentic applications, enabling AI agents to collaborate and perform tasks autonomously or with human oversight. Figure: Agentic patterns supported from Autogen (Source) Key Features Asynchronous Messaging: Facilitates communication between agents through asynchronous messages, supporting both event-driven and request/response interaction patterns. Scalable & Distributed Architecture: Allows the design of complex, distributed agent networks capable of operating across organizational boundaries, enhancing scalability and resilience. Modular & Extensible Design: Enables customization with pluggable components, including custom agents, tools, memory, and models, promoting flexibility in system development. Building an AI Agent with AutoGen To create an AI agent using AutoGen, follow these steps: Install the Required Packages: pip install 'autogen-agentchat==0.4.0.dev8' 'autogen-ext[openai]==0.4.0.dev8' Define and Run the Agent: import asyncio from autogen_agentchat.agents import AssistantAgent from autogen_agentchat.ui import Console from autogen_agentchat.conditions import TextMentionTermination from autogen_agentchat.teams import RoundRobinGroupChat from autogen_ext.models import OpenAIChatCompletionClient # Define a tool async def get_weather(city: str) -> str: return f"The weather in {city} is 73 degrees and Sunny." async def main() -> None: # Define an agent weather_agent = AssistantAgent( name="weather_agent", model_client=OpenAIChatCompletionClient( model="gpt-4o-2024-08-06", # api_key="YOUR_API_KEY", ), tools=[get_weather], ) # Define termination condition termination = TextMentionTermination("TERMINATE") # Define a team agent_team = RoundRobinGroupChat([weather_agent]) # Start the conversation console = Console() await console.start(agent_team, termination) # Example interaction await console.send_message("What's the weather like in New York?") if __name__ == "__main__": asyncio.run(main()) This example demonstrates how to: Define a tool: The get_weather function simulates a weather API call. Create an agent: The weather_agent is an instance of AssistantAgent with access to the get_weather tool. Set up termination conditions: The TextMentionTermination condition terminates the conversation when the word "TERMINATE" is mentioned. Form a team: The RoundRobinGroupChat creates a team of agents that take turns responding. Initiate the conversation: The Console class starts the conversation with the agent team, and an example interaction is shown by sending a message to the team. This code snippet showcases how to use AutoGen to develop an agentic workflow where agents can interact with tools, make decisions, and communicate with each other or users in a structured manner. Areas Where AutoGen Excels Multi-Agent Collaboration: AutoGen facilitates the development of systems where multiple AI agents can converse and collaborate to accomplish tasks, enhancing the capabilities of LLM applications. Enhanced LLM Inference & Optimization: The framework supports advanced inference APIs, improving performance and reducing costs associated with large language models. User Feedback Platform users appreciate AutoGen's flexibility and scalability in building complex AI agent systems. The modular design and support for asynchronous messaging are highlighted as significant advantages. However, some users note that the learning curve can be steep for beginners, suggesting that comprehensive documentation and tutorials benefit new adopters. For more detailed information and case studies, refer to the official AutoGen documentation and the Microsoft Research project page. CrewAI CrewAI is an open-source Python framework designed to orchestrate role-playing, autonomous AI agents, enabling them to collaborate effectively on complex tasks. CrewAI empowers agents to work seamlessly by fostering collaborative intelligence and tackling sophisticated workflows. Key Features Role-Based Agents: Agents can assume distinct roles and personas, enhancing their ability to understand and interact with complex systems. Autonomous Decision-Making: Agents make independent decisions based on context and available tools, streamlining processes without constant human oversight. Seamless Collaboration: Agents share information and resources to achieve common goals, functioning as a cohesive unit. Complex Task Management: Designed to handle intricate tasks such as multi-step workflows, decision-making, and problem-solving. Building an AI Agent with CrewAI Install the Required Packages: Ensure you have Python >=3.10 <=3.13 installed on your system.  First, install CrewAI: pip install crewai To include additional tools for agents, use: pip install 'crewai[tools]' Define and Run an AI Agent: CrewAI utilizes YAML configuration files to define agents and tasks. Here's how to set up a simple crew: 1. Create a New Crew Project: crewai create crew my_project This command creates a new project folder with the following structure: my_project/ ├── .gitignore ├── pyproject.toml ├── README.md ├── .env └── src/ └── my_project/ ├── __init__.py ├── main.py ├── crew.py ├── tools/ │ ├── custom_tool.py │ └── __init__.py └── config/ ├── agents.yaml └── tasks.yaml 2. Modify the agents.yaml File: Define your agents with specific roles and goals. For example: # src/my_project/config/agents.yaml researcher: role: "AI Researcher" goal: "Uncover cutting-edge developments in AI" backstory: "A seasoned researcher with a knack for uncovering the latest developments in AI." 3. Modify the tasks.yaml File: Define tasks assigned to agents. For example: # src/my_project/config/tasks.yaml research_task: description: "Conduct thorough research on the latest AI trends." expected_output: "A list of the top 5 AI developments in 2024." agent: researcher 4. Run the Crew Navigate to the project directory and execute: python src/my_project/main.py This will initiate the agents and execute the defined tasks. Areas Where CrewAI Excels Multi-Agent Collaboration: CrewAI enables the creation of AI agents with distinct roles and goals, facilitating complex task execution through collaboration. Extensibility: The framework allows for integrating custom tools and APIs, enabling agents to interact with external services and data sources. User Feedback Users appreciate CrewAI's ability to orchestrate multiple AI agents effectively, highlighting its role-based architecture and flexibility in handling complex workflows. The framework's extensibility and support for custom tools are also significant advantages. For more detailed information and case studies, refer to the official CrewAI documentation. AgentGPT AgentGPT is an autonomous AI platform that enables users to create and deploy customizable AI agents directly from a web browser. These agents are designed to perform tasks independently, breaking down complex objectives into manageable sub-tasks and executing them sequentially to achieve the desired goals. Key Features Customizable AI Agents: Users can tailor agents by assigning specific goals and parameters, allowing the AI to adapt to various needs. User-Friendly Interface: The platform offers an intuitive interface with pre-designed templates for common tasks, facilitating quick and efficient setup without extensive technical expertise. Real-Time Processing: AgentGPT operates in real-time, enabling immediate feedback and interaction, which enhances user engagement and efficiency. Building an AI Agent with AgentGPT: AgentGPT is designed to be accessible directly through web browsers, eliminating the need for local installations. Users can access the platform online without installing additional packages. Define and Run an AI Agent: Access the Platform: Navigate to the AgentGPT website using a web browser. Configure the Agent: Set Objectives: Provide a clear goal or task for the agent. Customize Parameters: Adjust settings to tailor the agent's behavior to specific requirements. Deploy the Agent: Initiate the agent's operation, allowing it to perform the defined tasks autonomously. This example demonstrates creating and deploying an AI agent capable of autonomously executing tasks to achieve specified objectives. Areas Where AgentGPT Excels Accessibility: Its web-based interface makes it versatile and accessible across different platforms without additional installations. User-Friendly Design: The platform is designed to be user-friendly, making it accessible to both tech-savvy developers and those without extensive technical backgrounds. User Feedback Users appreciate AgentGPT's versatility and accessibility, noting its user-friendly interface and the ability to customize agents for various applications. However, some users have expressed a desire for more advanced features and integrations to enhance functionality. For more detailed information and case studies, refer to the official AgentGPT documentation. MetaGPT MetaGPT is an open-source multi-agent framework that orchestrates AI agents, each assigned specific roles, to collaboratively tackle complex tasks. By encoding Standard Operating Procedures (SOPs) into prompt sequences, MetaGPT emulates human-like workflows, enhancing coherence and efficiency in problem-solving. Key Features Role Assignment: Designates distinct roles to AI agents—such as product managers, architects, and engineers—mirroring the structure of a traditional software company. Standardized Workflows: Implements SOPs to guide agent interactions, ensuring systematic and organized task execution. Iterative Development: Facilitates continuous refinement through executable feedback, allowing agents to improve outputs iteratively. Building an AI Agent with MetaGPT Ensure Python 3.9 or higher is installed. Install MetaGPT using pip: pip install metagpt Define and Run an AI Agent To develop a simple application, such as a "To-Do List" application, follow these steps: Create a New Project Directory: mkdir todo_app cd todo_app Initialize the Project: metagpt init This command sets up the project structure with the necessary configuration files. Configure Agents and SOPs: Modify the agents.yaml file to assign roles and tasks: agents: - name: ProductManager role: Define project scope and features - name: Architect role: Design system architecture - name: Engineer role: Implement features Run the MetaGPT Framework metagpt run This command initiates the agents to develop the "To-Do List" application collaboratively. This example demonstrates how to create a collaborative environment where AI agents, each with specific roles, work together to develop a software application, emulating a human software development team. Areas Where MetaGPT Excels Complex Task Management: MetaGPT effectively decomposes and manages intricate tasks by assigning specialized roles to agents. Error Mitigation: The framework's structured approach reduces logic inconsistencies and errors, enhancing the reliability of outputs. User Feedback Users commend MetaGPT for its innovative approach to multi-agent collaboration, highlighting its ability to emulate human organizational structures and improve problem-solving efficiency. However, some note that the framework's complexity may present a learning curve for new users. For more detailed information and case studies, refer to the official MetaGPT documentation. Future Trends in Agentic AI The evolution of agentic AI is poised to transform various sectors through advancements in multi-agent collaboration, integration with emerging technologies, and enhanced human-agent partnerships. Advances in Multi-Agent Collaboration Recent research emphasizes the importance of effective communication and coordination among AI agents to tackle complex tasks. Frameworks like AgentVerse facilitate dynamic interactions among agents, enabling them to adjust their roles and strategies collaboratively, thereby enhancing problem-solving efficiency.  Additionally, studies on connectivity-driven communication have shown that structured information sharing among agents leads to improved coordination and task performance. Integration with Emerging Technologies The convergence of AI with blockchain and the Internet of Things (IoT) creates decentralized, intelligent systems. Integrating blockchain with AI enhances data security and transparency, facilitating decentralized decision-making. Furthermore, combining AI with IoT devices improves agents' perception capabilities, allowing for real-time data processing and more informed decisions. Human-Agent Collaboration Enhancing the symbiotic relationship between humans and AI agents is crucial for effective decision support. Studies indicate that transparency in AI systems fosters trust, leading to better collaborative outcomes.  Moreover, research on human-AI co-learning highlights that collaborative experiences can trigger mutual learning pathways, improving human and AI performance. The future of agentic AI lies in the seamless integration of multi-agent systems, emerging technologies, and human collaboration, paving the way for more autonomous and intelligent systems. Key Takeaways: AI Agents Transformative Potential: Agentic AI is revolutionizing industries by automating intricate workflows, optimizing decision-making, and reducing operational costs in applications like customer service, finance, and robotics. Core Components of Agentic AI: These systems rely on four pillars—Perception, Decision-Making, Learning, and Action—to process data, adapt to environments, and execute tasks autonomously. Single-Agent vs. Multi-Agent Systems: While single-agent systems focus on individual tasks, multi-agent systems enable collaboration and scalability in complex scenarios, such as traffic management or swarm robotics. Frameworks Powering Agentic AI: Leading frameworks like AutoGen, CrewAI, AgentGPT, and MetaGPT provide tools to design, deploy, and optimize agentic systems for various applications. Future Trends: The evolution of agentic AI will emphasize multi-agent collaboration, integration with technologies like IoT and blockchain, and enhanced human-AI partnerships for seamless and transparent interactions. This guide highlights the potential and practicality of agentic AI, offering a roadmap for leveraging these systems to drive innovation and efficiency across industries. Learn more about Encord Agents: Efficiently integrate humans, SOTA models, and your own models into data workflows to reduce the time taken to achieve high-quality data annotation at scale.

Human-in-the-Loop (HITL) is a transformative approach in AI development that combines human expertise with machine learning to create smarter, more accurate models. Whether you're training a computer vision system or optimizing machine learning workflows, this guide will show you how HITL improves outcomes, addresses challenges, and accelerates success in real-world applications. In machine learning and computer vision training, Human-in-the-Loop (HITL) is a concept whereby humans play an interactive and iterative role in a model's development. To create and deploy most machine learning models, humans are needed to curate and annotate the data before it is fed back to the AI. The interaction is key for the model to learn and function successfully. Human annotators, data scientists, and data operations teams always play a role. They collect, supply, and annotate the necessary data. However, how the amount of input differs depends on how involved human teams are in the training and development of a computer vision model.  What Is Human in the Loop (HITL)? Human-in-the-loop (HITL) is an iterative feedback process whereby a human (or team) interacts with an algorithmically-generated system, such as computer vision (CV), machine learning (ML), or artificial intelligence (AI). Every time a human provides feedback, a computer vision model updates and adjusts its view of the world. The more collaborative and effective the feedback, the quicker a model updates, producing more accurate results from the datasets provided in the training process.  In the same way, a parent guides a child’s development, explaining that cats go “meow meow” and dogs go “woof woof” until a child understands the difference between a cat and a dog.  Here's a way to create human-in-the-loop workflows in Encord. How Does Human-in-the-loop Work? Human-in-the-loop aims to achieve what neither an algorithm nor a human can manage by themselves. Especially when training an algorithm, such as a computer vision model, it’s often helpful for human annotators or data scientists to provide feedback so the models gets a clearer understanding of what it’s being shown.  In most cases, human-in-the-loop processes can be deployed in either supervised or unsupervised learning. In supervised learning, HITL model development, annotators or data scientists give a computer vision model labeled and annotated datasets.  HITL inputs then allow the model to map new classifications for unlabeled data, filling in the gaps at a far greater volume with higher accuracy than a human team could. Human-in-the-loop improves the accuracy and outputs from this process, ensuring a computer vision model learns faster and more successfully than without human intervention.  In unsupervised learning, a computer vision model is given largely unlabeled datasets, forcing them to learn how to structure and label the images or videos accordingly. HITL inputs are usually more extensive, falling under a deep learning exercise category.  Here are 5 ways to build successful data labeling operations. Active Learning vs. Human-In-The-Loop Active learning and human-in-the-loop are similar in many ways, and both play an important role in training computer vision and other algorithmically-generated models. Yes, they are compatible, and you can use both approaches in the same project.  However, the main difference is that the human-in-the-loop approach is broader, encompassing everything from active learning to labeling datasets and providing continuous feedback to the algorithmic model.  How Does HITL Improve Machine Learning Outcomes? The overall aim of human-in-the-loop inputs and feedback is to improve machine-learning outcomes.  With continuous human feedback and inputs, the idea is to make a machine learning or computer vision model smarter. With constant human help, the model produces better results, improving accuracy and identifying objects in images or videos more confidently.  In time, a model is trained more effectively, producing the results that project leaders need, thanks to human-in-the-loop feedback. This way, ML algorithms are more effectively trained, tested, tuned, and validated.  Are There Drawbacks to This Type of Workflow? Although there are many advantages to human-in-the-loop systems, there are drawbacks too.   Using HITL processes can be slow and cumbersome, while AI-based systems can make mistakes, and so can humans. You might find that a human error goes unnoticed and then unintentionally negatively impacts a model's performance and outputs. Humans can’t work as quickly as computer vision models.  Hence the need to bring machines onboard to annotate datasets. However, once you’ve got people more deeply involved in the training process for machine learning models, it can take more time than it would if humans weren’t as involved.  Examples of Human-in-the-Loop AI Training One example is in the medical field, with healthcare-based image and video datasets. A 2018 Stanford study found that AI models performed better with human-in-the-loop inputs and feedback compared to when an AI model worked unsupervised or when human data scientists worked on the same datasets without automated AI-based support.  Humans and machines work better and produce better outcomes together. The medical sector is only one of many examples whereby human-in-the-loop ML models are used.  When undergoing quality control and assurance checks for critical vehicle or airplane components, an automated, AI-based system is useful; however, for peace of mind, having human oversight is essential.  Human-in-the-loop inputs are valuable whenever datasets are rare and a model is being fed. Such as a dataset containing a rare language or artifacts. ML models may not have enough data to draw from; human inputs are invaluable for training algorithmically-generated models.   A Human-in-the-Loop Platform for Computer Vision Models With the right tools and platform, you can get a computer vision model to production faster.  Encord is one such platform, a collaborative, active learning suite of solutions for computer vision that can also be used for human-in-the-loop (HITL) processes.  With AI-assisted labeling, model training, and diagnostics, you can use Encord to provide the perfect ready-to-use platform for a HITL team, making accelerating computer vision model training and development easier. Collaborative active learning is at the core of what makes human-in-the-loop (HITL) processes so effective when training computer vision models. This is why it’s smart to have the right platform at your disposal to make this whole process smoother and more effective.  We also have Encord Active, an open-source computer vision toolkit, and an Annotator Training Module that will help teams when implementing human-in-the-loop iterative training processes.  ‍At Encord, our active learning platform for computer vision is used by a wide range of sectors - including healthcare, manufacturing, utilities, and smart cities - to annotate human pose estimation videos and accelerate their computer vision model development. Encord is a comprehensive AI-assisted platform for collaboratively annotating data, orchestrating active learning pipelines, fixing dataset errors, and diagnosing model errors & biases. Try it for free today. 

In this guide, we compare three of the top ECG annotation tools to train AI in the healthcare industry. From automated annotations to beginner-friendly features, we’ll explore how each tool empowers the future of cardiac care. Machine learning has made waves within the medical community and healthcare industry. Artificial Intelligence (AI) has proven itself useful in numerous uses across a variety of domains, from Radiology and Gastroenterology to Histology and Surgery.  The frontier has now hit Electrocardiography (ECG) ECG is a cornerstone of cardiac diagnostics, offering a window into the electrical activity of the heart. By capturing this activity as waveforms, ECG provides vital insights into heart health, helping clinicians detect and monitor conditions such as arrhythmias, ischemia, and hypertrophy. With an annotation tool, you can annotate the different waves on your Electrocardiogram diagrams and train machine learning models to recognize patterns in the data. The effectiveness of these models hinges on the quality of their training data—specifically, the annotations that mark key features of ECG waveforms. In the guide below, we’ll explore three leading ECG annotation tools, examining how they enhance the annotation process and contribute to the future of cardiac care. Compare Top ECG Annotation Tools In the sections below, we'll provide some information about three top ECG annotation tools, covering their features, benefits and who they might work best for. The three tools we will be reviewing today are: Encord ECG OHIT ECG Viewer WaveformECG Here is an overview of them:   Encord ECG Encord is an automated and collaborative annotation platform for medical companies looking at ECG Annotation, DICOM/NIfTI annotation, video annotation, and dataset management. It's the best option for teams that are: Looking for automated, semi-automated or AI-assisted image and video annotation. Annotating all ontologies. Working with other medical modalities such as DICOM and NIfTI. Wanting one place to easily manage annotators, track performance, and create QA/QC workflows. Benefits & Key features: Use-case-centric annotations — from native DICOM & NIfTI annotations for medical imaging to ECG Annotation tool for ECG Waveforms. Allows for point and time interval annotations. Supports the Bioportal Ontology such as PR and QT intervals. Integrated data labeling services. Integrated MLOps workflow for computer vision and machine learning teams. Easy collaboration, annotator management, and QA workflows — to track annotator performance and increase label quality. Robust security functionality — label audit trails, encryption, FDA, CE Compliance, and HIPAA compliance. Advanced Python SDK and API access (+ easy export into JSON and COCO formats). Best for teams who: Are graduating from an in-house solution or open-source tool and need a robust, secure, and collaborative platform to scale their annotation workflows. Haven't found an annotation platform that can actually support their use case as well as they'd like (such as building complex nested ontologies, or rendering ECG waveforms). Team looking to build artificial neural networks for the healthcare industry. AI-focused cardiology start-ups or mature companies looking to expand their machine-learning practices should consider the Encord tool. Pricing: Free trial model, and simple per-user pricing after that. OHIF ECG Viewer The OHIF ECG Viewer provides can be found from Radical Imaging’s Github. The tool provides a streamlined annotation experience and native image rendering with the ability to perform measurements of all relevant ontologies. It is easy to export annotations or create a report for later investigation. The tool does not support any dataset management or collaboration which might be an issue for more sophisticated and mature teams. For a cardiologist just getting started this is a great tool and provides a baseline for comparing to other tools. Benefits & Key features: Leader in open-source software. Renders ECG waveform natively. Easy (& free) to get started labeling images with. Great for manual ECG annotation. Best for: Teams just getting started. Pricing: Free. WaveformECG The WaveformECG tool is a web-based tool for managing and analyzing ECG data. The tool provides a streamlined annotation experience and native image rendering with the ability to perform measurements of all relevant ontologies. It is easy to export annotations or create a report for later investigation. The tool does not support any dataset management or collaboration which might be an issue for more sophisticated and mature teams. So if you're new to the deep learning approach to ECG annotations the WaveformECG tool might be useful but if you’re looking at more advanced artificial neural networks or deep neural networks it might not be the best place. Benefits & Key features: Allows for point and time interval annotations and citations. Supports the Bioportal Ontology and metrics. Annotations are stored with the waveforms, ready for data analysis. Renders ECG waveform natively. Supports scrolling through each ECG waveform. Best for: Researchers and students. Pricing: Free. Why are ECG Annotations Important in Medical Research? ECG annotation is an essential aspect of medical research and diagnosis, involving the identification and interpretation of different features in the ECG waveform. It plays a critical role in the accurate diagnosis and treatment of heart conditions and abnormalities, allowing you to detect a wide range of heart conditions, including arrhythmias, ischemia, and hypertrophy. Through the meticulous analysis of the ECG waveform, experts can identify any irregularities in the electrical activity of the heart, accurately determining the underlying cause of a patient's symptoms. The information gleaned from ECG annotation provides vital indicators of heart health, including heart rate, rhythm, and electrical activity. Regular ECG monitoring is invaluable in the management of patients with chronic heart conditions such as atrial fibrillation or heart failure. Here ECG annotation assists experts in identifying changes in heart rhythm or other abnormalities that may indicate a need for treatment adjustment or further diagnostic testing. With regular ECG monitoring and annotation, clinicians can deliver personalized care, tailoring interventions to the unique needs of each patient. ECG Annotations Examples The first open-source frameworks have been developed to build models based on ECG data e.g. Deep-Learning Based ECG Annotation. In this example, the author automated the process of annotating peaks of ECG waveforms using a recurrent neural network in Keras. Even though the model was not 100% performant (it struggles to get the input/output right). It seems to work well on the QT database of PhysioNet. The Authors does mention it fails in some cases that it has never seen. Potential future development of machine learning would be to play with augmenting the ECGs themselves or create synthetic data. Another example of how deep learning and machine learning is useful in ECG waveforms can be found in the MathWorks Waveform Segmentation guide. Using a Long Short-Term Memory (LSTM) network, MathWorks achieved impressive results as seen in the confusion matrix below: If you want to get started yourself you can find a lot of open-source ECG datasets, e.g. the QT dataset from PhysioNet. How can Machine Learning Support ECG Annotations? Machine learning has significant potential in supporting and automating the analysis of ECG waveforms, providing a powerful tool for clinicians for improving the accuracy and efficiency of ECG interpretation.  By utilizing machine learning algorithms, ECG waveforms can be automatically analyzed and annotated, assisting clinicians in detecting and diagnosing heart conditions and abnormalities faster and at higher accuracy. One of the main benefits of machine learning in ECG analysis is the ability to process vast amounts of patient data. By analyzing large datasets, machine learning algorithms can identify patterns and correlations that may be difficult or impossible for humans to detect. This can assist in the identification of complex arrhythmias or other subtle changes in the ECG waveform that may indicate underlying heart conditions. Additionally, machine learning algorithms can help in the detection of abnormalities or changes in the ECG waveform over time, facilitating the early identification of chronic heart conditions. By comparing ECG waveforms from different time points, machine learning algorithms can detect changes in heart rate, rhythm, or other features that may indicate a need for treatment adjustment or further diagnostic testing. Lastly, machine learning models can be trained to recognize patterns in ECG waveforms that may indicate specific heart conditions or abnormalities. For example, an algorithm could be trained to identify patterns that indicate an increased risk of a heart attack or other acute cardiac event. By analyzing ECG waveforms and alerting clinicians to these patterns, it can help in the early identification and treatment of these conditions, potentially saving lives. Conclusion There you have it! The 3 Best ECG annotation Tools for machine learning in 2023.  We’re super excited to see the frontier being pushed on ECG waveforms in machine learning and proud to be part of the journey with our customers. If you’re looking into augmenting the ECGs themselves or creating synthetic data get in touch and we can provide you input and help with it! 📌 See why healthcare organizations and top AI companies trust Encord for end-to-end computer vision solutions. Detect biases, fix dataset errors, and streamline your model training in a collaborative platform. Try Encord for Free Today.

The global speech and voice recognition market was worth USD 12.62 billion in 2023. Moreover, estimates suggest it will be worth USD 26.8 billion by 2025.  The rising popularity of voice assistants results from sophisticated generative audio artificial intelligence (AI) tools that enhance human-machine interaction. Yet, the full potential and diverse applications of audio AI remain underexplored, continually evolving to meet the shifting needs and preferences of businesses and consumers alike. In this post, we will discuss audio AI, its capabilities, applications, inner workings, implementation challenges, and how Encord can help you curate audio data to build scalable audio AI systems. What is Audio AI? Audio AI refers to deep neural networks that process, analyze, and predict audio signals. The technology is witnessing significant adoption in various industries like media, healthcare, security, and smart devices.  It enables organizations to build tools like virtual assistants with advanced functionalities such as automated transcription, translation, and audio enhancement to optimize human interactions with sound. Capabilities of Audio AI Audio AI is still evolving, with different AI algorithms and frameworks emerging to allow users to produce high-quality audio content. The list below highlights audio AI’s most powerful capabilities, which are valuable in diverse use cases. Text-to-Speech (TTS): TTS technology converts written text into lifelike speech. Modern TTS systems use neural networks to produce highly natural and expressive voices, enabling applications in virtual assistants, audiobooks, and accessibility tools for individuals with visual impairments. Voice Cloning: Voice cloning replicates a person’s voice with minimal training data. AI models can create AI voices that closely mimic the original speaker by analyzing speech patterns and vocal characteristics. The method is valuable in personalized customer experiences, voiceover work, and preserving voices for historical or sentimental purposes. Voice Generation: AI-driven synthesis generates new voices, often used for creative projects or branding. Experts can tailor these AI-generated voices for tone, emotion, and style, opening opportunities in marketing, gaming, and virtual character creation. Voice Dubbing: Audio AI facilitates seamless dubbing by synchronizing translated speech with original audio while maintaining the speaker's tone and expression. The approach enhances the accessibility of movies, TV shows, and educational content across languages. Audio Editing and Generation: AI-powered tools simplify audio editing by automating background noise reduction, equalization, and sound enhancement. Generative deep-learning models create music and sound effects. They serve as versatile tools for content creators and musicians, helping them produce unique and immersive auditory experiences to captivate audiences. Speech-to-text transcription: Audio AI converts spoken language into accurate written text. The ability helps automate tasks like transcribing meeting minutes, generating video subtitles, and assigning real-time captions. Voice Assistants and Chatbots: Users can leverage audio AI to develop intelligent voice assistants and chatbots to enable seamless, conversational interactions with end customers. These systems handle tasks like setting reminders, answering queries, and assisting with customer support.  Emotion Recognition in Speech: Deep learning audio architectures can analyze vocal tone, pitch, and rhythm to detect emotions in speech. This technology is valuable in customer service to gauge satisfaction, mental health monitoring to assess well-being, and entertainment to create emotionally aware systems. Sound Event Detection: Experts can use audio AI to identify specific sounds, such as alarms, footsteps, or breaking glass, in real time. This capability is crucial for security systems, smart homes, and industrial monitoring. Music Recommendation: Intelligent audio systems can generate personalized music recommendations by analyzing listening habits, preferences, and contextual data. Applications of Audio AI Audio AI advancements are empowering businesses to leverage the technology across a wide range of applications. The following sections mention a few popular use cases where audio AI’s capabilities transform user experience. Audio AI in the Film Industry Audio AI in the film industry helps film-makers in the following ways: Dubbing: Audio AI makes the dubbing process more efficient and accurate, allowing natural lip-syncing and emotion-rich translations and making films accessible to global audiences. Animated movies: AI-generated voices can bring characters to life in animated films. They offer diverse vocal styles without requiring extensive audio recording sessions. Music: Audio AI assists in composing original scores. This helps improve background soundscapes and automates audio mixing for immersive experiences. Audio AI for Content Generation Audio AI streamlines content creation workflows across platforms by automating and enhancing audio production. Below are a few examples: Podcasts: Audio AI helps reduce background noise, balance audio levels, and even generate intro music scores according to the creator’s specifications. Creators can also use AI to simulate live editing, making real-time adjustments during recording, such as muting background disruptions or improving voice clarity. YouTube and TikTok videos: AI-powered tools enable creators to effortlessly add voiceovers in deepfakes, captions, and sound effects. This can make content more engaging and professional for different target audiences. Audiobooks: Text-to-speech (TTS) technology delivers lifelike narrations, reducing production time while maintaining high-quality storytelling. AI can also adapt narrations for diverse listener needs, such as adjusting speaking speed or adding environmental sounds, for more personalization and inclusivity. Audio AI in Healthcare Healthcare professionals can improve patient care and documentation through audio AI automation. Some common use cases include: Patient engagement: AI-powered voice assistants can interact with patients to provide appointment reminders, medication alerts, and health education, ensuring better adherence to care plans.  Managing Documentation: Audio AI automates documentation by transcribing doctor-patient conversations and generating accurate medical records in real-time. This approach reduces administrative burdens on healthcare providers and allows them to provide personalized care according to each patient’s needs. Audio AI in the Automotive Industry The automotive sector uses audio AI to make vehicles smarter and more user-friendly. A few innovative applications include: Auto diagnostics: Audio AI can analyze engine or mechanical sounds to detect anomalies. This helps identify potential issues early and reduces maintenance costs. In-car entertainment: With Audio AI, drivers can use voice to control a vehicle’s audio systems, personalizing music playlists, adjusting audio settings, and enhancing sound quality for an immersive experience. Smart home integration: Users can control their vehicles from home devices like Alexa or Google Home via voice commands. With a stable internet connection, they can start the engine, lock or unlock doors, check fuel levels, and set navigation destinations. Audio AI in Education Education offers numerous opportunities where Audio AI can enhance the learning experience for both students and teachers. The most impactful applications include: Lecture Transcription: Instead of manually taking notes, students can feed a teacher’s recorded lecture into an audio AI model to transcribe the recording into a written document. Automated Note-taking: AI-based audio applications can generate notes by listening to lectures in real-time. This allows the student to focus more on listening to the lecturer. Real-time Translation: Instructors can use AI-powered translation tools to break language barriers, making educational content accessible to a global audience. Audio and Video Summarization: Audio AI software allows students to condense lengthy materials into concise highlights, saving time and improving comprehension. Captioning of Virtual Classes: Students with hearing impairments or those in noisy environments can use audio AI to caption online lectures for better understanding. How Audio AI Works As mentioned earlier, Audio AI uses machine learning algorithms to analyze sounds. It understands sound datasets through waveforms and spectrograms to detect patterns. Waveform A waveform represents sound as amplitude across time. The amplitude is the height of a wave indicating a specific sound’s loudness. Waveforms can consist of extensive data points containing amplitude values for each second. The dataset can range from 44,000 to 96,000 samples. Spectrogram: Color Variations Represent Amplitudes In contrast, a spectrogram is a much richer representation that includes a sound’s amplitude and frequency against time. Since each data point in a spectrogram contains more information than a point in a waveform, analyzing spectrograms requires fewer samples and computational power. The choice of using spectrograms or waveforms as inputs to generative models depends on the desired output and raw audio complexity. Waveforms are often helpful when you need phase information to process multiple sounds simultaneously. Phases indicate the precise timing of a point in a wave. Audio AI Model Architectures Besides output and raw audio type, the model architecture is a crucial component of audio AI systems. Several architectures are available to help generate sounds and voices for the use cases discussed in the previous section. The list below discusses the most popular frameworks implemented in modern audio AI tools. Variational Autoencoders (VAEs) VAEs are deep learning models comprising encoder and decoder modules. The encoder converts data samples into a latent distribution, while the decoder module samples from this distribution to generate the output. VAE Architecture Experts train VAEs by minimizing a reconstruction loss. They compare the decoder's generated output with the original input. The goal is to ensure the decoder accurately reconstructs an original sound sample by randomly sampling from the latent distribution. Generative Adversarial Networks (GANs) GANs consist of a generator and a discriminator component. The generator creates random noise, such as sound, voice, or music, and sends it to the discriminator. The discriminator tries to tell whether the generated noise is real or fake. GAN Architecture The training process involves the generator producing multiple samples and the discriminator trying to distinguish between real and fake samples. Training stops once the discriminator cannot categorize the generator’s output as fake. Transformers Transformers are one of the most revolutionary and sophisticated deep learning architectures. They use the well-known attention mechanism to generate or predict an output. The architecture powers most modern large language models (LLMs) we know today. Transformer Architecture Relates Input and Masked Output Embeddings using Multi-head Attention to Predict the Next Sample in the Output Sequence The attention mechanism works by understanding the relationships between different existing data points to predict or generate a new sample. It breaks down a soundwave or any other data type into smaller chunks or embeddings to detect relations. It uses this information to identify which part of data is most significant to generate a specific output. Learn how vision transformers (Vit) work in our detailed guide   Challenges of Audio AI Although audio AI models’ predictive and generative power is constantly improving, developing them is challenging. The following section highlights the most common issues in building audio AI solutions. Data Preparation High-quality data is essential for training effective audio AI systems. However, preparing audio data means cleaning, labeling, and segmenting large datasets. This can be time-consuming and resource-intensive. Variations in accents, noise levels, and audio quality further complicate data management. It requires robust preprocessing techniques to ensure models use diverse and representative data for optimal performance. Data Privacy Audio data often contains sensitive personal information, such as the voices of real individuals or confidential conversations. Ensuring data privacy is a significant challenge, as improper handling could lead to breaches or misuse. Companies must comply with strict regulations, implement anonymization techniques, and adopt secure storage and processing methods to protect user data and build trust. Accuracy and Bias Audio AI systems can struggle with accuracy due to diverse accents, languages, or environmental noise. Additionally, biases in training data can lead to uneven performance across demographics, potentially creating disadvantages for certain groups. Addressing these issues requires datasets from several groups to ensure fair, consistent, and relevant results across all user profiles. Continuous Adaptation Languages evolve and differ across generations, having various slang, acronyms, and conversational styles. Continuously adapting audio AI tools to match new user requirements is tricky, and failing to keep up can result in outdated or irrelevant outputs. Continuous learning, model updates, and retraining are essential but demand significant resources, technical expertise, and robust infrastructure to maintain system relevance and effectiveness over time. Multimodal Support and Integration Applications like TTS, transcription, narrations, and translation require multimodal models that simultaneously understand different data modalities, such as text, speech, and images. However, integrating audio AI with such modalities presents technical challenges. Seamless multimodal support requires sophisticated architectures capable of processing and aligning diverse data types. Ensuring interoperability between systems while maintaining efficiency and accuracy adds complexity to implementation, especially for real-time systems like virtual assistants or multimedia tools. Encord for Audio AI Addressing all the above challenges can be overwhelming, requiring different approaches, expertise, and infrastructure. However, a business can take a more practical route using cost-effective audio annotation tools that streamline data management and model development workflows. Encord’s audio annotation tool is a comprehensive multimodal AI data platform that enables the efficient management, curation and annotation of large-scale unstructured datasets including audio files, videos, images, text, documents and more. Encord supports a number of audio annotation use cases such as speech recognition, emotion detection, sound event detection and whole audio file classification. Teams can also undertake multimodal annotation such as analyzing and labeling text and images alongside audio files. Encord Key Features Flexible Classification: Allows for precise classification of multiple attributes within a single audio file down to the millisecond. Overlapping Annotations: Supports layered annotations, enabling the labeling of multiple sound events or speakers simultaneously. Collaboration Tools: Facilitates team collaboration with features like real-time progress tracking, change logs, and review workflows. Efficient Editing: Provides tools for revising annotations based on specific time ranges or classification types. AI-Assisted Annotation: Integrates AI-driven tools to assist with pre-labeling and quality control, improving the speed and accuracy of annotations. Strength The platform’s support for complex, multilayered annotations, real-time collaboration, and AI-driven annotation automation, along with its ability to handle various file types like WAV and an intuitive UI with precise timestamps, makes Encord a flexible, scalable solution for AI teams of all sizes preparing audio data for AI model development. Learn how to use Encord to annotate audio data   Audio AI: Key Takeaways Audio AI technology holds great promise, with exciting opportunities to improve user experience and business profitability in different domains. However, implementing audio AI requires careful planning and robust tools to leverage its full potential. Below are some key points to remember regarding audio AI. Audio AI Applications: Businesses can use audio AI to streamline film production, generate podcasts and videos, manage vehicles, improve patient engagement, and make education more inclusive. Audio AI Challenges: Audio AI’s most significant challenges include preparing data, maintaining security, ensuring accuracy and unbiased output, continuously adapting to change, and integrating with multimodal functionality. Encord for Audio AI: Encord’s versatile data curation features can help you quickly clean, preprocess, and label audio data to train high-performing and scalable AI models.

Breaking: Meta has recently released Segment Anything Model 2 (SAM 2) & SA-V Dataset. Update: Segment Anything (SAM) is live in Encord! Check out our tutorial on How To Fine-Tune Segment Anything or book a demo with the team to learn how to use the Segment Anything Model (SAM) to reduce labeling costs. If you thought the AI space was moving fast with tools like ChatGPT, GPT-4, and Stable Diffusion, then 2024 has truly shifted the game to a whole new level. Meta’s FAIR lab broke ground with the Segment Anything Model (SAM), a state-of-the-art image segmentation model that has since become a cornerstone in computer vision. Released in 2023, SAM is based on foundation models that have significantly impacted natural language processing (NLP). It focuses on promptable segmentation tasks, using prompt engineering to adapt to diverse downstream segmentation problems. Fast forward to December 2024, and SAM has inspired a wave of innovations across AI, from real-time video segmentation in augmented reality (AR) to integration with multimodal systems like OpenAI’s GPT-4 Vision and Anthropic’s multimodal Claude. Whether you're working in design, healthcare, or robotics, SAM’s impact is still expanding. In this blog post, you will:  Learn how it compares to other foundation models. Learn about the Segment Anything (SA) project Dive into SAM's network architecture, design, and implementation. Learn how to implement and fine-tune SAM Discover potential uses of SAM for AI-assisted labeling. Why Are We So Excited About SAM?  Having tested it out for a day now, we can see the following incredible advances: SAM can segment objects by simply clicking or interactively selecting points to include or exclude from the object. You can also create segmentations by drawing bounding boxes or segmenting regions with a polygon tool, and it will snap to the object. When encountering uncertainty in identifying the object to be segmented, SAM can produce multiple valid masks. SAM can identify and generate masks for all objects present in an image automatically. After precomputing the image embeddings, SAM can provide a segmentation mask for any prompt instantly for real-time interaction with the model. Segment Anything Model Vs. Previous Foundation Models SAM is a big step forward for AI because it builds on the foundations that were set by earlier models. SAM can take input prompts from other systems, such as, in the future, taking a user's gaze from an AR/VR headset to select an object, using the output masks for video editing, abstracting 2D objects into 3D models, and even popular Google Photos tasks like creating collages. It can handle tricky situations by generating multiple valid masks where the prompt is unclear. Take, for instance, a user’s prompt for finding Waldo: Image displaying semantic segmentations by the Segment Anything Model (SAM) One of the reasons the results from SAM are groundbreaking is because of how good the segmentation masks are compared to other techniques like ViTDet. The illustration below shows a comparison of both techniques: Segmentation masks by humans, ViTDet, and the Segment Anything Model (SAM) Read the Segment Anything research paper for a detailed comparison of both techniques. What is the Segment Anything Model (SAM)? SAM, as a vision foundation model, specializes in image segmentation, allowing it to accurately locate either specific objects or all objects within an image. SAM was purposefully designed to excel in promptable segmentation tasks, enabling it to produce accurate segmentation masks based on various prompts, including spatial or textual clues that identify specific objects. Is Segment Anything Model Open Source? The short answer is YES! The SA-1B Dataset has been released as open source for research purposes. In addition, Meta AI released the pre-trained models (~2.4 GB in size) and code under Apache 2.0 (a permissive license) following FAIR’s commitment to open research. It is freely accessible on GitHub. The training dataset is also available, alongside an interactive demo web UI. FAIR Segment Anything (SA) Paper How does the Segment Anything Model (SAM) work? SAM's architectural design allows it to adjust to new image distributions and tasks seamlessly, even without prior knowledge, a capability referred to as zero-shot transfer. Utilizing the extensive SA-1B dataset, comprising over 11 million meticulously curated images with more than 1 billion masks, SAM has demonstrated remarkable zero-shot performance, often surpassing previous fully supervised results. Before we dive into SAM’s network design, let’s discuss the SA project, which led to the introduction of the very first vision foundation model. Segment Anything Project Components The Segment Anything (SA) project is a comprehensive framework for image segmentation featuring a task, model, and dataset.  The SA project starts with defining a promptable segmentation task with broad applicability, serving as a robust pretraining objective, and facilitating various downstream applications. This task necessitates a model capable of flexible prompting and the real-time generation of segmentation masks to enable interactive usage. Training the model effectively required a diverse, large-scale dataset. The SA project implements a "data engine" approach to address the lack of web-scale data for segmentation. This involves iteratively using our efficient model to aid in data collection and utilizing the newly gathered data to enhance the model's performance. Let’s look at the individual components of the SA project. Segment Anything Task  The Segment Anything Task draws inspiration from natural language processing (NLP) techniques, particularly the next token prediction task, to develop a foundational model for segmentation. This task introduces the concept of promptable segmentation, wherein a prompt can contain various ,input forms such as foreground/background points, bounding boxes, masks, or free-form text, indicating what objects to segment in an image. The objective of this task is to generate valid segmentation masks based on any given prompt, even in ambiguous scenarios. This approach enables a natural pre-training algorithm and facilitates zero-shot transfer to downstream segmentation tasks by engineering appropriate prompts.  Using prompts and composition techniques allows for extensible model usage across a wide range of applications, distinguishing it from interactive segmentation models designed primarily for human interaction. Dive into SAM's Network Architecture and Design SAM’s design hinges on three main components: The promptable segmentation task to enable zero-shot generalization. The model architecture. The dataset that powers the task and model. Leveraging concepts from Transformer vision models, SAM prioritizes real-time performance while maintaining scalability and powerful pretraining methods. Scroll down to learn more about SAM’s network design. Foundation model architecture for the Segment Anything (SA) model Task SAM was trained on millions of images and over a billion masks to return a valid segmentation mask for any prompt. In this case, the prompt is the segmentation task and can be foreground/background points, a rough box or mask, clicks, text, or, in general, any information indicating what to segment in an image. The task is also used as the pre-training objective for the model. Model SAM’s architecture comprises three components that work together to return a valid segmentation mask: An image encoder to generate one-time image embeddings. A prompt encoder that embeds the prompts. A lightweight mask decoder that combines the embeddings from the prompt and image encoders. Components of the Segment Anything (SA) model We will dive into the architecture in the next section, but for now, let’s take a look at the data engine. Segment Anything Data Engine The Segment Anything Data Engine was developed to address the scarcity of segmentation masks on the internet and facilitate the creation of the extensive SA-1B dataset containing over 1.1 billion masks. A data engine is needed to power the tasks and improve the dataset and model. The data engine has three stages: Model-assisted manual annotation, where professional annotators use a browser-based interactive segmentation tool powered by SAM to label masks with foreground/background points. As the model improves, the annotation process becomes more efficient, with the average time per mask decreasing significantly. Semi-automatic, where SAM can automatically generate masks for a subset of objects by prompting it with likely object locations, and annotators focus on annotating the remaining objects, helping increase mask diversity. The focus shifts to increasing mask diversity by detecting confident masks and asking annotators to annotate additional objects. This stage contributes significantly to the dataset, enhancing the model's segmentation capabilities. Fully automatic, where human annotators prompt SAM with a regular grid of foreground points, yielding on average 100 high-quality masks per image. The annotation becomes fully automated, leveraging model enhancements and techniques like ambiguity-aware predictions and non-maximal suppression to generate high-quality masks at scale. This approach enables the creation of the SA-1B dataset, consisting of 1.1 billion high-quality masks derived from 11 million images, paving the way for advanced research in computer vision. Segment Anything 1-Billion Mask Dataset The Segment Anything 1 Billion Mask (SA-1B) dataset is the largest labeled segmentation dataset to date. It is specifically designed for the development and evaluation of advanced segmentation models. We think the dataset will be an important part of training and fine-tuning future general-purpose models. This would allow them to achieve remarkable performance across diverse segmentation tasks. For now, the dataset is only available under a permissive license for research. The SA-1B dataset is unique due to its: Diversity Size High-quality annotations Diversity The dataset is carefully curated to cover a wide range of domains, objects, and scenarios, ensuring that the model can generalize well to different tasks. It includes images from various sources, such as natural scenes, urban environments, medical imagery, satellite images, and more.  This diversity helps the model learn to segment objects and scenes with varying complexity, scale, and context. Distribution of images and masks for training Segment Anything (SA) model Size The SA-1B dataset, which contains over a billion high-quality annotated images, provides ample training data for the model. The sheer volume of data helps the model learn complex patterns and representations, enabling it to achieve state-of-the-art performance on different segmentation tasks. Relative size of SA-1B to train the Segment Anything (SA) model High-Quality Annotations The dataset has been carefully annotated with high-quality masks, leading to more accurate and detailed segmentation results. In the Responsible AI (RAI) analysis of the SA-1B dataset, potential fairness concerns and biases in geographic and income distribution were investigated. The research paper showed that SA-1B has a substantially higher percentage of images from Europe, Asia, and Oceania, as well as middle-income countries, compared to other open-source datasets. It's important to note that the SA-1B dataset features at least 28 million masks for all regions, including Africa. This is 10 times more than any previous dataset's total number of masks. Distribution of the images to train the Segment Anything (SA) model At Encord, we think the SA-1B dataset will enter the computer vision hall of fame (together with famous datasets such as COCO, ImageNet, and MNIST) as a resource for developing future computer vision segmentation models. Notably, the dataset includes downscaled images to mitigate accessibility and storage challenges while maintaining significantly higher resolution than many existing vision datasets. The quality of the segmentation masks is rigorously evaluated, with automatic masks deemed high quality and effective for training models, leading to the decision to include automatically generated masks exclusively in SA-1B. Let's delve into the SAM's network architecture, as it's truly fascinating. Segment Anything Model's Network Architecture The Segment Anything Model (SAM) network architecture contains three crucial components: the Image Encoder, the Prompt Encoder, and the Mask Decoder. Architecture of the Segment Anything (SA) universal segmentation model Image Encoder At the highest level, an image encoder (a masked auto-encoder, MAE, pre-trained Vision Transformer, ViT) generates one-time image embeddings. It is applied before prompting the model. The image encoder, based on a masked autoencoder (MAE) pre-trained Vision Transformer (ViT), processes high-resolution inputs efficiently. This encoder runs once per image and can be applied before prompting the model for seamless integration into the segmentation process. Prompt Encoder The prompt encoder encodes background points, masks, bounding boxes, or texts into an embedding vector in real-time. The research considers two sets of prompts: sparse (points, boxes, text) and dense (masks).  Points and boxes are represented by positional encodings and added with learned embeddings for each prompt type. Free-form text prompts are represented with an off-the-shelf text encoder from CLIP. Dense prompts, like masks, are embedded with convolutions and summed element-wise with the image embedding. The prompt encoder manages both sparse and dense prompts. Dense prompts, like masks, are embedded using convolutions and then summed element-wise with the image embedding. Sparse prompts, representing points, boxes, and text, are encoded using positional embeddings for each prompt type. Free-form text is handled with a pre-existing text encoder from CLIP. This approach ensures that SAM can effectively interpret various types of prompts, enhancing its adaptability. Mask Decoder A lightweight mask decoder predicts the segmentation masks based on the embeddings from both the image and prompt encoders. The mask decoder efficiently maps the image and prompt embeddings to generate segmentation masks. It maps the image embedding, prompt embeddings, and an output token to a mask. SAM uses a modified decoder block and then a dynamic mask prediction head, taking inspiration from already existing Transformer decoder blocks. This design incorporates prompt self-attention and cross-attention mechanisms to update all embeddings effectively. All of the embeddings are updated by the decoder block, which uses prompt self-attention and cross-attention in two directions (from prompt to image embedding and back). SAM's Mask Decoder is optimized for real-time performance, enabling seamless interactive prompting of the model. The masks are annotated and used to update the model weights. This layout enhances the dataset and allows the model to learn and improve over time, making it efficient and flexible. SAM's overall design prioritizes efficiency, with the prompt encoder and mask decoder running seamlessly in web browsers in approximately 50 milliseconds, enabling real-time interactive prompting. How to Use the Segment Anything Model? At Encord, we see the Segment Anything Model (SAM) as a game changer in AI-assisted labeling. It basically eliminates the need to go through the pain of segmenting images with polygon drawing tools and allows you to focus on the data tasks that are more important for your model.  These other data tasks include mapping the relationships between different objects, giving them attributes that describe how they act, and evaluating the training data to ensure it is balanced, diverse, and bias-free. Enhancing Manual Labeling with AI SAM can be used to create AI-assisted workflow enhancements and boost productivity for annotators. Here are just a few improvements we think SAM can contribute: Improved accuracy: Annotators can achieve more precise and accurate labels, reducing errors and improving the overall quality of the annotated data. Faster annotation: There is no doubt that SAM will speed up the labeling process, enabling annotators to complete tasks more quickly and efficiently when combined with a suitable image annotation tool. Consistency: Having all annotators use a version of SAM would ensure consistency across annotations, which is particularly important when multiple annotators are working on the same project. Reduced workload: By automating the segmentation of complex and intricate structures, SAM significantly reduces the manual workload for annotators, allowing them to focus on more challenging and intricate tasks. Continuous learning: As annotators refine and correct SAM's assisted labeling, we could implement it such that the model continually learns and improves, leading to better performance over time and further streamlining the annotation process. As such, integrating SAM into the annotation workflow is a no-brainer from our side, and it would allow our current and future customers to accelerate the development of cutting-edge computer vision applications. AI-Assisted Labeling with Segment Anything Model on Encord Consider the medical image example from before to give an example of how SAM can contribute to AI-assisted labeling. We uploaded the DICOM image to the demo web UI, and spent 10 seconds clicking the image to segment the different areas of interest. Afterward, we did the same exercise with manual labeling using polygon annotations, which took 2.5 minutes. A 15x improvement in the labeling speed! Read the blog How to use SAM to Automate Data Labeling in Encord for more information.   Combining SAM with Encord Annotate harnesses SAM's versatility in segmenting diverse content alongside Encord's robust ontologies, interactive editing capabilities, and extensive media compatibility. Encord seamlessly integrates SAM for annotating images, videos, and specialized data types like satellite imagery and DICOM files. This includes support for various medical imaging formats, such as X-ray, CT, and MRI, requiring no extra input from the user. How to Fine-Tune Segment Anything Model (SAM) To fine-tune the SAM model effectively, particularly considering its intricate architecture, a pragmatic strategy involves refining the mask decoder. Unlike the image encoder, which boasts a complex architecture with numerous parameters, the mask decoder offers a streamlined and lightweight design. This characteristic makes it inherently easier, faster, and more memory-efficient to fine-tune. Users can expedite the fine-tuning process while conserving computational resources by prioritizing adjustments to the mask decoder. This focused approach ensures efficient adaptation of SAM to specific tasks or datasets, optimizing its performance for diverse applications. The steps include: Create a custom dataset by extracting the bounding box coordinates (prompts for the model) and the ground truth segmentation masks. Prepare for fine-tuning by converting the input images to PyTorch tensors, a format SAM's internal functions expect. Run the fine-tuning step by instantiating a training loop that uses the lightweight mask decoder to iterate over the data items, generate masks, and compare them to your ground truth masks. The mask decoder is easier, faster, and more memory-efficient to fine-tune. Compare your tuned model to the original model to make sure there’s indeed a significant improvement. To see the fine-tuning process in practice, check out our detailed blog, How To Fine-Tune Segment Anything, which also includes a Colab notebook as a walkthrough. Real-World Use Cases and Applications SAM can be used in almost every single segmentation task, from instance to panoptic. We’re excited about how quickly SAM can help you pre-label objects with almost pixel-perfect segmentation masks before your expert reviewer adds the ontology on top.  From agriculture and retail to medical imagery and geospatial imagery, the AI-assisted labeling that SAM can achieve is endless. It will be hard to imagine a world where SAM is not a default feature in all major annotation tools. This is why we at Encord are very excited about this new technology. Find other applications that could leverage SAM below.  Image and Video Editors SAM’s outstanding ability to provide accurate segmentation masks for even the most complex videos and images can provide image and video editing applications with automatic object segmentation skills. Whether the prompts (point coordinates, bounding boxes, etc.) are in the foreground or background, SAM uses positional encodings to indicate if the prompt is in the foreground or background. Generating Synthetic Datasets for Low-resource Industries One challenge that has plagued computer vision applications in industries like manufacturing is the lack of datasets. For example, industries building car parts and planning to detect defects in the parts along the production line cannot afford to gather large datasets for that use case. You can use SAM to generate synthetic datasets for your applications. If you realize SAM does not work particularly well for your applications, an option is to fine-tune it on existing datasets. Interested in synthetic data? Read our article, What Is Synthetic Data Generation and Why Is It Useful? Gaze-based Segmentation AR applications can use SAM’s Zero-Shot Single Point Valid Mask Evaluation technique to segment objects through devices like AR glasses based on where subjects gaze. This can help AR technologies give users a more realistic sense of the world as they interact with those objects. Medical Image Segmentation SAM's application in medical image segmentation addresses the demand for accurate and efficient ROI delineation in various medical images. While manual segmentation is accurate, it's time-consuming and labor-intensive. SAM alleviates these challenges by providing semi- or fully automatic segmentation methods, reducing time and labor, and ensuring consistency.  With the adaptation of SAM to medical imaging, MedSAM emerges as the first foundation model for universal medical image segmentation. MedSAM leverages SAM's underlying architecture and training process, providing heightened versatility and potentially more consistent results across different tasks. The MedSAM model consistently does better than the best segmentation foundation models when tested on a wide range of internal and external validation tasks that include different body structures, pathological conditions, and imaging modalities. This underscores MedSAM's potential as a powerful tool for medical image segmentation, offering superior performance compared to specialist models and addressing the critical need for universal models in this domain. MedSAM is open-sourced, and you can find the code for MedSAM on GitHub. Where Does This Leave Us? The Segment Anything Model (SAM) truly represents a groundbreaking development in computer vision. By leveraging promptable segmentation tasks, SAM can adapt to a wide variety of downstream segmentation problems using prompt engineering.  This innovative approach, combined with the largest labeled segmentation dataset to date (SA-1B), allows SAM to achieve state-of-the-art performance in various segmentation tasks. With the potential to significantly enhance AI-assisted labeling and reduce manual labor in image segmentation tasks, SAM can pave the way in industries such as agriculture, retail, medical imagery, and geospatial imagery.  At Encord, we recognize the immense potential of SAM, and we are soon bringing the model to the Encord Platform to support AI-assisted labeling, further streamlining the data annotation process for users. As an open-source model, SAM will inspire further research and development in computer vision, encouraging the AI community to push the boundaries of what is possible in this rapidly evolving field.  Ultimately, SAM marks a new chapter in the story of computer vision, demonstrating the power of foundation models in transforming how we perceive and understand the world around us. A Brief History of Meta's AI & Computer Vision As one of the leading companies in the field of artificial intelligence (AI), Meta has been pushing the boundaries of what's possible with machine learning models: from recently released open source models such as LLaMA to developing the most used Python library for ML and AI, PyTorch. Advances in Computer Vision Computer vision has also experienced considerable advancements, with models like CLIP bridging the gap between text and image understanding. These models use contrastive learning to map text and image data. This allows them to generalize to new visual concepts and data distributions through prompt engineering.  FAIR’s Segment Anything Model (SAM) is the latest breakthrough in this field. Their goal was to create a foundation model for image segmentation that can adapt to various downstream tasks using prompt engineering. The release of SAM started a wave of vision foundation models and vision language models like LLaVA, GPT-4 vision, Gemini, and many more. Let’s briefly explore some key developments in computer vision that have contributed to the growth of AI systems like Meta's. Convolutional Neural Networks (CNNs) CNNs, first introduced by Yann LeCun (now VP & Chief AI Scientist at Meta) in 1989, have emerged as the backbone of modern computer vision systems, enabling machines to learn and recognize complex patterns in images automatically.  By employing convolutional layers, CNNs can capture local and global features in images, allowing them to recognize objects, scenes, and actions effectively. This has significantly improved tasks such as image classification, object detection, and semantic segmentation. Generative Adversarial Networks (GANs) GANs are a type of deep learning model that Ian Goodfellow and his team came up with in 2014. They are made up of two neural networks, a generator, and a discriminator, competing. The generator aims to create realistic outputs, while the discriminator tries to distinguish between real and generated outputs. The competition between these networks has resulted in the creation of increasingly realistic synthetic images. It has led to advances in tasks such as image synthesis, data augmentation, and style transfer. Transfer Learning and Pre-trained Models Like NLP, computer vision has benefited from the development of pre-trained models that can be fine-tuned for specific tasks. Models such as ResNet, VGG, and EfficientNet have been trained on large-scale image datasets, allowing researchers to use these models as a starting point for their own projects. The Growth of Foundation Models Foundation models in natural language processing (NLP) have made significant strides in recent years, with models like Meta’s own LLaMA or OpenAI’s GPT-4 demonstrating remarkable capabilities in zero-shot and few-shot learning.  These models are pre-trained on vast amounts of data and can generalize to new tasks and data distributions by using prompt engineering. Meta AI has been instrumental in advancing this field, fostering research and the development of large-scale NLP models that have a wide range of applications. Here, we explore the factors contributing to the growth of foundation models. Large-scale Language Models The advent of large-scale language models like GPT-4 has been a driving force behind the development of foundation models in NLP. These models employ deep learning architectures with billions of parameters to capture complex patterns and structures in the training data.  Transfer Learning A key feature of foundation models in NLP is their capacity for transfer learning. Once trained on a large corpus of data, they can be fine-tuned on smaller, task-specific datasets to achieve state-of-the-art performance across a variety of tasks. Zero-shot and Few-shot Learning Foundation models have also shown promise in zero-shot and few-shot learning, where they can perform tasks without fine-tuning or with minimal task-specific training data. This capability is largely attributed to the models' ability to understand and generate human-like responses based on the context provided by prompts. Multi-modal Learning Another growing area of interest is multi-modal learning, where foundation models are trained to understand and generate content across different modalities, such as text and images.  Models like Gemini, GPT-4V, LLaVA, CLIP, and ALIGN show how NLP and computer vision could be used together to make multi-modal models that can translate actions from one domain to another. Read the blog Google Launches Gemini, Its New Multimodal AI Model to learn about the recent advancements in multimodal learning. Ethical Considerations and Safety The growth of foundation models in NLP has also raised concerns about their ethical implications and safety. Researchers are actively exploring ways to mitigate potential biases, address content generation concerns, and develop safe and controllable AI systems. Proof of this was the recent call for a six-month halt on all development of cutting-edge models. Frequently Asked Questions on Segment Anything Model (SAM) How do I fine-tune SAM for my tasks? We have provided a step-by-step walkthrough you can follow to fine-tune SAM for your tasks. Check out the tutorial: How To Fine-Tune Segment Anything. What datasets were used to train SAM? The Segment Anything 1 Billion Mask (SA-1B) dataset has been touted as the “ImageNet of segmentation tasks.” The images vary across subject matter. Scenes, objects, and places frequently appear throughout the dataset. Masks range from large-scale objects such as buildings to fine-grained details like door handles. See the data card and dataset viewer to learn more about the composition of the dataset. Does SAM work well for all tasks? Yes. You can automatically select individual items from images—it works very well on complex images. SAM is a foundation model that provides multi-task capabilities to any application you plug it into. Does SAM work well for ambiguous images? Yes, it does. Because of this, you might find duplicates in your mask sets when you run SAM over your dataset. This allows you to select the most appropriate masks for your task. In this case, you should add a post-processing step to segment the most suitable masks for your task. How long does it take SAM to generate segmentation masks? SAM can generate a segment in as little as 50 milliseconds—practically real-time! Do I need a GPU to run SAM? Although it is possible to run SAM on your CPU, you can use a GPU to achieve significantly faster results from SAM.

With unstructured data like text files and documents comprising 80% of all datasets, implementing robust data management solutions is essential to extracting valuable insights from this vast amount of information. One crucial source of such data is PDF documents, which comprise a significant chunk of an organization’s digital archive. These documents include invoices, reports, contracts, research papers, presentations, and client briefs.  Companies can extract relevant data from these documents and use them in machine learning (ML) models to improve products and business operations. However, PDF text extraction is complex due to the varied nature of documents. In this post, we will discuss text extraction for ML models, its techniques, applications, challenges, and steps to build an efficient extraction process. We will also see how Encord can streamline these processes to achieve faster and more accurate results. Why High-quality Text Extraction Matters for Robust ML Models High-quality text extraction is essential for building robust ML and artificial intelligence (AI) models, as their accuracy and reliability heavily depend on the quality of the training data. Poorly extracted text can introduce noise, such as missing characters, misaligned structure, or incorrect semantics. These factors prevent a model's algorithms from learning hidden data patterns effectively and cause the model to overfit limited data samples. Accurate data extraction preserves context, structure, and meaning, producing better feature representation and model performance. It increases training data quality and reduces preprocessing efforts to streamline ML workflows for developing state-of-the-art (SOTA) natural language processing (NLP) frameworks and large language models (LLMs). Role of AI in Text Extraction Different text layouts, lengths, and document formats make text extraction challenging. Manual data entry approaches to collect data from them can be error-prone and time-consuming. A robust data extraction process requires significant automation to extract the desired samples from multiple sources with high accuracy. Modern AI-based methods offer a cost-effective alternative by allowing developers to quickly extract data from various document types while ensuring consistency across the entire extraction pipeline. The methods include deep learning techniques to intelligently identify and draw out relevant information from complex, unstructured formats like PDFs, scanned documents, or images. The list below summarizes the most significant benefits of using AI models for text extraction: Accuracy: AI models minimize human errors in text parsing due to inconsistent formatting or varying layouts. They maintain text integrity by accurately recognizing characters, preserving structure, and extracting meaningful content, even from noisy or low-quality inputs. Scalability: AI systems can effortlessly handle long-form documents. This makes them ideal for organizations like banks or research institutions that process thousands of PDFs daily. Better Customer Experience: Automated text extraction speeds up data-driven services like document validation or invoice processing. The method enables faster responses to customer needs and improved service quality. Faster Decision-Making: AI-based extraction optimizes document management and maintains information accuracy. These factors ensure the executive team can make informed decisions quickly. Automated PDF Text Extraction Techniques While automated methods rely heavily on machine learning algorithms to process and analyze textual data, the precise techniques can vary according to the use case. Two key approaches include optical character recognition (OCR) and natural language processing (NLP). OCR (Optical Character Recognition) OCR technology is pivotal for extracting text from scanned or image-based PDFs by converting visual characters into machine-readable text. Advanced OCR systems can handle diverse fonts, languages, and handwritten text. Four Stages of OCR to Recognize the Image KNU 123 NLP (Natural Language Processing) NLP techniques allow experts to extract text by enabling them to perform deeper analysis for better contextual understanding. Specific applications include: Named Entity Recognition (NER): Identifies and categorizes entities like names, dates, and locations. It helps understand relationships between such entities and allows for metadata tagging. NER Sentiment Analysis: Analyzes the emotional tone of the text, providing insights for tasks like customer feedback analysis or market research. Sentiment Analysis Tagging Part-of-Speech (PoS) Tagging: Assigns grammatical roles to words, supporting syntactic analysis and other linguistic tasks. PoS Tagging Text Classification: Automatically categorizes extracted text into predefined labels. This helps in document organization and compliance checks. Text Classifier for Spam Filtering Translation: Translates text into different languages, expanding the utility of multilingual documents for global audiences. Translation Model Applications of Text Extraction PDF data extraction emerges as a transformative solution to alleviate this challenge as organizations grapple with an overwhelming influx of documents. This method helps convert raw text into actionable insights, making it easier to manage and use information. It's gaining popularity across various industries, each using it to streamline processes and boost productivity. The list below highlights these industries and how they use text extraction to boost productivity. Healthcare: Extracting information from medical records, lab reports, and prescriptions aids in patient data management, clinical research, and personalized care planning. Customer Service: Analyzing customer feedback from emails, surveys, or chat logs enables improved service delivery, sentiment tracking, and issue resolution. Academic Research: Automating content extraction from journals, theses, and reports simplifies literature reviews, knowledge discovery, and bibliometric analysis. Spam Filtering: Text extraction helps identify malicious or irrelevant content in emails and messages. This boosts communication efficiency and cybersecurity. Recommendation Systems: Extracted data from user reviews or product descriptions fuels recommendation algorithms, which improves personalization and user engagement. Legal: Text extraction streamlines the analysis of contracts, case files, and legal briefs. It facilitates compliance checks, risk assessments, and e-discovery processes. Education: Extracting text from course outlines, lecture notes, and curriculum textbooks supports digital learning platforms and personalized education tools. Fraud Detection: Extracting data from invoices, transaction logs, bank statements, or claims enables organizations to identify anomalies and prevent financial fraud. Challenges of Extracting Text from PDFs Although advancements in text extraction techniques make it easier to extract data from PDFs, a few challenges persist. The following sections discuss these issues in greater detail to provide more insights into the potential obstacles when working with PDF data. Document Quality and Size PDFs often vary in quality, especially when dealing with scanned or older documents. Low-resolution scans, faded text, or noisy backgrounds make text recognition difficult and inaccurate. Additionally, large file sizes can increase processing time and strain computational resources. Resolving such issues requires efficiently processing bulk documents through advanced tools and systematic procedures. Domain-Specific Information Extracting text from PDFs with specialized content, such as legal contracts, medical records, or financial statements, poses unique challenges. These documents often contain technical jargon, abbreviations, and context-dependent information that general extraction tools struggle to interpret accurately. Tailored solutions, incorporating domain-specific models and ontologies, are essential to ensure precise and meaningful extraction in such cases. Language Variety PDFs can include multilingual content or complex Chinese, Arabic, or Cyrillic scripts. Handling such variety requires AI models to support several languages and linguistic structures. Variations in grammar, syntax, and character sets further complicate the process. General-purpose algorithms may fail to capture hidden meanings, expressions, and nuances evident in a native language. Loss of Semantic Structure PDFs can contain glossaries, appendices, and other components in a fragmented or misaligned manner. For instance, a paragraph on the first page may refer to an appendix at the document’s end. The text may also include embedded links to special terms and background information. These factors complicate automated extraction, as the algorithm may fail to parse them, distorting a text’s meaning. It may also overlook essential elements like headings, tables, or hierarchical relationships, resulting in a disorganized output and inaccurate interpretations. Integration with multimodal frameworks Many PDFs combine text with images, charts, or graphs to add context and elaborations on specific concepts. Extracting meaningful information from such multimodal content requires frameworks to process textual and visual data seamlessly. However, integrating an extraction tool with these frameworks is tricky. It calls for architectures that simultaneously process text and visuals to derive meaning. Steps to Build a Text Extraction Pipeline Organizations can mitigate the above challenges by building a robust end-to-end pipeline to extract text from multiple PDF files. The pipeline can consist of AI tools and techniques needed to ensure smooth data extraction. Although each use case will demand a different tool stack, the steps below can help you develop your desired extraction pipeline. Define Business Objectives Start by identifying the primary goals of the text extraction pipeline. This could include building LLMs, automating data entry, or enhancing decision-making. Clearly defining objectives helps prioritize features, such as extraction accuracy, processing speed, or integration with other systems. It helps develop relevant performance metrics, set realistic timelines, and target achievement expectations. Select Document Sources Identify and categorize the document sources from which you plan to extract texts, such as databases, online repositories, and email systems. You must also identify the document type that each source generates. Examples include invoices, legal contracts, research papers, and customer feedback forms. Understanding how each source varies will help tailor the extraction process to handle specific formats, layouts, and content types. This will ensure more accurate results and enhance scalability. Data Ingestion Once you identify the relevant sources, the next step is data ingestion, where you develop a method to collect the required documents. This step involves deciding whether to ingest documents in batches or real time. With batch ingestion, you collect documents in groups at regular intervals, while real-time ingestion lets you fetch documents the moment users create them. Batch ingestion is more straightforward to implement, requires less computational resources, and can handle large volumes of data. However, real time ingestion is more appropriate for time-sensitive applications such as live customer interactions. Document Preprocessing After data ingestion, you must process the collected documents to ensure they are in usable format for AI applications. Document processing can include: Text Extraction: You must choose between OCR and NLP techniques to extract text from documents. The goal is to convert unstructured or semi-structured information into machine-readable text for further processing. Data Cleaning: Cleaning removes errors, inconsistencies, or irrelevant information from the extracted text. It ensures the data is accurate, complete, and noise-free, enhancing its quality for training ML models. Data Transformation: This step converts cleaned text into a standardized format. It may also include tokenization, stemming, lemmatization, or structuring the text into vectors or tables to ensure compatibility with processing tools. Data Annotation: Accurate annotation is critical for building high-quality training data for supervised and semi-supervised learning. In this step, you must clearly label or tag extracted data with relevant information, such as named entities, categories, or sentiments. Text Data Storage Once you have a clean and annotated text dataset, it is essential to choose a suitable storage method for efficient retrieval and analysis. Standard storage solutions include databases and file systems with structured access management features. Depending on the data's volume and format, options may include relational databases, NoSQL databases, or cloud storage. Implement proper indexing and attach relevant metadata for streamlined management to ensure scalability and fast access. Utilization in ML/AI Models After storage, you must develop mechanisms to feed the stored textual data to AI models for training. The step may require adding libraries to your pipeline to automatically split the data into training, validation, and test sets. You can also include data analysis and visualization features to help developers examine data distributions, types, and other features relevant to specific use cases. Deployment and Post-Production Monitoring The final step after training is to push high-performing models to production and continuously monitor their output to identify issues. The pipeline can contain functions and APIs to compute performance metrics identified in the first step. It can detect issues such as data drift, text extraction quality, and latency and notify developers to help them resolve problems quickly. Learn more about text annotation in our comprehensive guide   Encord for PDF Text Extraction Implementing a text extraction pipeline from scratch can be complex and resource-intensive. It requires significant expertise in data architecture, ML/AI engineering, and systems integration. Additionally, it demands a deep understanding of document processing, data quality management, and scalable infrastructure to ensure smooth operations. While organizations can use open-source tools to build their desired pipeline, these solutions often offer limited functionality. A better alternative is to invest in a third-party platform that provides a comprehensive, all-in-one solution tailored to meet specific business needs. Encord is one option that can help you develop robust text extraction solutions for building large-scale LLMs and NLP platforms. It is an end-to-end AI-based multimodal data management and evaluation solution that allows you to build scalable document processing pipelines for different applications. Encord Index: Unify petabytes of unstructured data from all local and cloud data sources to one platform for in-depth data management, visualization, search and granular curation. Leverage granular metadata filtering, sort and search using quality metrics, and natural language queries to explore all your data in one place. Encord Annotate: Leverage SOTA AI-assisted labeling workflows and flexibly setup complex ontologies to efficiently and accurately label computer vision/multimodal data for training, fine-tuning and aligning AI models at scale. Encord Active: Evaluate and validate Al models to surface, curate, and prioritize the most valuable data for training and fine-tuning to supercharge Al model performance. Leverage automatic reporting on metrics like mAP, mAR, and F1 Score. Combine model predictions, vector embeddings, visual quality metrics and more to automatically reveal errors in labels and data. Encord Key Features Support for petabyte-scale datasets: Encord helps curate and explore extensive documents through metadata-based granular filtering and natural language search features. It can handle various document types and organize them according to their contents. Document Annotation: The platform lets you annotate and classify text with Encord agents, allowing you to customize labeling workflows according to your use case. It supports text classification, NER, PDF text extraction, sentiment analysis, question-answering, and translation. You can also build nested relationship structures in your data schema to improve the quality of annotations. Multimodal Support: Encord is a fully integrated multimodal framework that can help you integrate text extraction pipelines with other modalities, such as audio, images, videos, and DICOM. Data Security: The platform is compliant with major regulatory frameworks, such as the General Data Protection Regulation (GDPR), System and Organization Controls 2 (SOC 2 Type 1), AICPA SOC, and Health Insurance Portability and Accountability Act (HIPAA) standards. It also uses advanced encryption protocols to ensure adherence to data privacy standards. Seamless data synchronization: You can connect Encord with your native cloud data storage platforms and programmatically control workflows using the Encord Python SDK. Ease-of-Use: Encord offers an easy-to-use user interface with self-explanatory menu options and powerful search functionality for quick data discovery. Users can query large scale datasets in everyday language to search for images and use relevant filters for efficient data retrieval. G2 Review Encord has a rating of 4.8/5 based on 60 reviews. Users highlight the tool’s simplicity, intuitive interface, and several annotation options as its most significant benefits.  However, they suggest a few areas for improvement, including more customization options for tool settings and faster model-assisted labeling. Overall, Encord’s ease of setup and quick return on investments make it popular among AI experts. Want to find the best tool to annotate PDFs? Here is our list of the Top 8 Document Annotation Tools.   PDF Text Extraction: Key Takeaways As more businesses turn to NLP frameworks and LLMs to optimize business operations, the need for automated text extraction pipelines will increase to help them build high-performing AI solutions. However, building a text extraction framework will be a significant challenge as document volume and variety increase. Below are a few key points regarding PDF text extraction: Importance of High-quality PDF Text Extraction: With high-quality text extraction, businesses can ensure they get accurate and consistent data to train AI models. PDF Text Extraction Challenges: Differences in document quality and size, domain-specific terminology, diverse languages, complex semantic structures, and the presence of visuals in documents make text extraction difficult. Encord for PDF Text Extraction: Encord is a robust document curation and annotation platform that can handle large documents and provide multimodal support for streamlined text extraction. If you're extracting images and text from PDFs to build a dataset for your multimodal AI model, be sure to explore Encord's Document Annotation Tool—to train and fine-tune high-performing NLP Models and LLMs.

Discover how platforms like Encord are revolutionizing multimodal data labeling, empowering medical AI teams to unlock groundbreaking insights and improve patient outcomes. Labeling multimodal data is becoming crucial across various fields, especially in the medical industry. Developing sophisticated AI models now demands multimodal datasets, which involve processing audio, video, text, medical imaging, and other data types within a unified, consistent structure. With the recent addition of robust platform support for document and audio data, alongside the multimodal annotation editor, Encord is now empowering customers to seamlessly manage and label these complex multimodal datasets. Examples of multimodal medical AI data What does multimodal mean for the medical industry? Multimodal medical data can take several forms: DICOM Files and Medical Imaging (CT, MRI, X-rays) Medical imaging, stored in the DICOM (Digital Imaging and Communications in Medicine) format, forms the backbone of modern healthcare. By labeling these images alongside their corresponding reports, AI systems can be trained to correlate visual features with the descriptive information in the reports, enabling more accurate and insightful analyses. Electronic Health Records (EHR), Lab Results, and Genomic Data EHRs consolidate a patient’s medical history, lab results, and treatment records, while genomic data offers insights into genetic predispositions and potential responses to therapies. Together, these multimodal datasets enable personalized medicine, allowing AI to predict disease progression, recommend treatments, and optimize patient outcomes by combining genetic, biochemical, and historical data. Textual Data from Clinical Notes and Reports Physicians often document observations, diagnoses, and treatment plans in free-text clinical notes and reports. Natural language processing (NLP) algorithms process these unstructured texts to extract valuable information, such as symptoms, medications, and treatment responses. When integrated with structured data, this enhances AI's ability to provide comprehensive patient assessments and treatment recommendations. Key Challenges in Integrating Multimodal Medical Data  Integrating multimodal medical data presents several challenges that can hinder the development of effective AI solutions.  Synchronizing Imaging Data Synchronizing imaging data, like CT or MRI scans, with non-imaging data, such as lab results or genomic information, involves aligning information from fundamentally different modalities, each with unique formats, structures, and temporal contexts. Imaging data is often large, high-dimensional, and requires precise metadata, such as timestamps, acquisition parameters, or patient positioning, to make sense of the images. Non-imaging data, on the other hand, is typically structured as numerical results, textual reports, or categorical labels, which may have been collected at different times, under varying conditions, or in separate systems. The complexity arises in ensuring that these disparate data types are properly aligned for meaningful analysis. For instance, a lab test result may need to be linked to an MRI scan taken on the same day to establish a correlation, but mismatched timestamps or incomplete metadata can hinder this process. Without robust synchronization, multimodal datasets risk losing context, making it challenging for AI models to learn accurate relationships between data types. This underscores the importance of tools and platforms that can automate and streamline the process of aligning and synchronizing multimodal data while preserving its clinical integrity. Inconsistency Across Different Data Types Another significant challenge is the inconsistency in formats and metadata across different data types—such as DICOM files, EHRs, and textual clinical notes. These variations make it difficult to establish a unified structure for data analysis and processing, as each data type comes with its own standards, organization, and level of detail. Overcoming these discrepancies is critical to ensure seamless integration and meaningful insights. The Importance of Labeling in Multimodal Medical AI Labeling is at the heart of developing successful multimodal medical AI systems. High-quality labels allow models to identify patterns, make accurate predictions, and generate reliable insights, which is especially critical in healthcare where precision is paramount. Labels for DICOM files, for example, must accurately reflect the clinical context and imaging features to guide model predictions. Annotating medical images and text also presents unique difficulties due to the complexity and diversity of data, requiring experts to handle subtle differences and ambiguous cases. The role of medical experts in this process cannot be overstated, as their domain knowledge ensures that annotations are both precise and clinically relevant. Accurate labeling is not just a technical requirement—it is a cornerstone of building AI systems that can deliver impactful, life-saving solutions in the medical field. How to Label and Analyze Multimodal Medical Files Encord offers a powerful solution for labeling multimodal medical data. In this guide, we’ll demonstrate its capabilities using an example that combines a CT scan with its corresponding medical report. Create a multimodal dataset First you must create a dataset in Encord that contains your multimodal medical files. Upload your files to Encord, and add them to a dataset. See our documentation for more information on uploading files and creating datasets. Ensure that you add custom metadata to your files so that they can be displayed correctly in the label editor. Documentation on how to add custom metadata can be found here. Create an ontology to label your files Next you must create an ontology to define what labels you can apply to files in your dataset. For our CT scan & medical report example the ontology contains bitmask objects for `Spine` and `Pelvis` to label the CT scan, as well as a bounding box for labeling a `Section of interest` in the PDF file. See our documentation here for more information on creating ontologies in Encord. Create a project Once your dataset and ontology are ready, the next step is to create a project. Choose or create a workflow to suit your needs, then attach your dataset and ontology to the project to get started. For more information on creating projects, see our documentation here. Set a custom editor layout In this example, we aim to label the CT scan alongside its corresponding medical report. To achieve this, we need to design a custom editor layout that displays the files side-by-side in the label editor. This layout leverages the custom metadata added to each file either during or after the upload process in Encord. For more information on creating custom editor layouts and attaching them to a project, see the end-to-end example in our documentation here. Label your data With everything ready, it’s time to start labeling your data. Open a DICOM file from the task queue to view it in the label editor alongside its corresponding medical report. Apply labels to the currently selected file. To add labels to the related file, click Annotate this tile before proceeding. Once both files are labeled, click Submit to finalize and submit them. Both files will then move to the review queue for further processing. See our documentation on how to label for more information. Key Takeaways  Multimodal medical AI data is diverse and complex: From DICOM files and imaging data to electronic health records and clinical notes, integrating and analyzing these diverse modalities is critical for advancing healthcare AI. Data labeling is foundational for success: Structured and accurately labeled datasets are essential for training high-performing AI models, especially in the medical field, where precision is paramount. Challenges of multimodal data require robust solutions: Issues like inconsistent formats, metadata mismatches, and synchronizing imaging with non-imaging data add complexity, but advanced platforms such as Encord can streamline these processes. High-quality annotations demand expertise and tools: Creating reliable labels for medical data often requires collaboration with domain experts and specialized tools, ensuring datasets are both accurate and clinically relevant. Platforms like Encord simplify multimodal data workflows: By supporting multiple data modalities, synchronizing disparate data types, and offering integrated annotation tools, Encord helps medical AI teams accelerate development and improve model performance.