Immerse yourself in vision

Imagine having a digital assistant that could browse the web, gather information, and complete tasks for you, all while you focus on more important things. That's the power of web agents, a new breed of AI systems, changing how we interact with the internet. Web agents use large language models (LLMs) – the reasoning layer required to understand and navigate the unstructured data space of the web. The LLMs allow agents to read, comprehend, and even write text, making them incredibly versatile. But why are web agents suddenly becoming so important?  In today's data-driven world, businesses are drowning in online information. Web agents offer a lifeline by automating research, data extraction, and content creation. They can sift through mountains of data in seconds, freeing up valuable time and resources. This blog post will dive deeper into web agents and LLMs. We'll explore how they work, the incredible benefits they offer, and how businesses can implement them to gain a competitive edge. Get ready to discover the future of online automation! Understanding How Web Agents & LLMs Work Core Components of a Web Agent Web agents are like specialized computer programs designed to automatically explore and interact with the internet. They are meant to perform tasks that normally require human interaction, such as browsing web pages, collecting data, and making decisions based on the information they find.   Think of a web agent as having several key functions: Crawling involves systematically browsing the web, following links, and exploring different pages. It's similar to how a search engine indexes the web, but web agents usually have a more specific goal in mind.   Parsing: When a web agent lands on a page, it must make sense of the content. Parsing involves analyzing the code and structure of the page to identify different elements, such as text, images, and links. Extracting: The web agent can extract the necessary information once the page is parsed. This could be anything from product prices on an e-commerce site to comments on a social media platform. By combining these functions, web agents can collect and process information from the web with minimal human intervention. When you add LLMs to the mix, web agents become even more powerful as they enable web agents to reason about the information they collect, make more complex decisions, and even converse with users. Role of LLMs in Interpreting Web Data LLMs can comprehend and reorganize raw textual information into structured formats, such as knowledge graphs or databases, by leveraging extensive training on diverse datasets. This process involves identifying the text's entities, relationships, and hierarchies, enabling more efficient information retrieval and analysis. The accuracy of LLMs in interpreting web data is heavily dependent on the quality and labeling of the training data. High-quality, labeled datasets provide the necessary context and examples for LLMs to learn the nuances of language and the relationships between different pieces of information.  Well-annotated data ensures that models can generalize from training examples to real-world applications, improving performance in tasks such as information extraction and content summarization. Conversely, poor-quality or unlabeled data can result in models that misinterpret information or generate inaccurate outputs. Interaction Between Web Agents and LLMs in Real-Time Web agents and LLMs interact dynamically to process and interpret web data in real time. Web agents continuously collect fresh data from various online sources and feed this information into LLMs.  This real-time data ingestion allows LLMs to stay updated with the latest information, enhancing their ability to make accurate predictions and decisions. For example, the WebRL framework trains LLM-based web agents through self-evolving online interactions, enabling them to effectively adapt to new data and tasks. Figure: An overview of the WebRL Framework (Source) The continuous feedback loop between web agents and LLMs facilitates the refinement of model predictions over time. As web agents gather new data and LLMs process this information, the models learn from any discrepancies between their predictions and actual outcomes.  This iterative learning process allows LLMs to adjust their internal representations and improve their understanding of complex web data. This leads to more accurate and reliable outputs in various applications, including content generation, recommendation systems, and automated customer service. Why Web Agents & LLMs Matter for Businesses In the evolving digital landscape, businesses increasingly leverage web agents to enhance operations and maintain a competitive edge. Their ability to aggregate, process, and analyze data in real-time empowers organizations to make smarter decisions and unlock new efficiencies.  Enhancing Data-Driven Decision-Making As autonomous software programs, web agents can systematically crawl and extract real-time data from various online sources. This capability enables businesses to gain timely market insights, monitor competitor activities, and track emerging industry trends.  By integrating this data into their decision-making processes, companies can make informed choices that align with current market dynamics. For instance, a business might deploy web agents to monitor social media platforms for customer sentiment analysis, allowing for swift adjustments to marketing strategies based on public perception. Such real-time data collection and analysis are crucial for staying responsive and proactive in a competitive market. Improving Operational Efficiency LLMs streamline operations by automating customer support, content moderation, and sentiment analysis tasks. This reduces the need for manual oversight while maintaining high accuracy levels. By leveraging better-prepared data, businesses can significantly lower operational costs while increasing team productivity. For example, customer support teams can focus on resolving complex issues while LLM-powered chatbots handle common queries. Competitive Advantage Through Continuous Learning Combining web agents and LLMs facilitates systems that continuously learn and adapt to new data. This dynamic interaction allows businesses to refine their models, improving predictions and decision-making accuracy.  Such adaptability is essential for long-term competitiveness, enabling companies to swiftly respond to changing market conditions and customer preferences. By investing in these technologies, businesses position themselves at the forefront of innovation, capable of leveraging AI-driven insights to drive growth and efficiency. Continuous learning ensures the systems evolve alongside the business, providing sustained value over time. Incorporating web agents and LLMs into business operations is not merely a technological upgrade but a strategic move towards enhanced decision-making, operational efficiency, and sustained competitive advantage. Building Web Agents: A Step-by-Step Architecture Guide The web agent architecture draws inspiration from the impressive work presented in the WebVoyager paper by He et al. (2024). Their research introduces a groundbreaking approach to building end-to-end web agents powered by LLMs.  By achieving a 59.1% task success rate across diverse websites, significantly outperforming previous methods, their architecture demonstrates the effectiveness of combining visual and textual understanding in web automation. Understanding the Core Components Let's explore how to build a web agent that can navigate websites like a human, breaking down each critical component and its significance. 1. The Browser Environment INITIALIZE browser with fixed dimensions SET viewport size to consistent resolution CONFIGURE automated browser settings Significance: Like giving the agent a reliable pair of eyes. The consistent viewport ensures the agent "sees" web pages the same way each time, making its visual understanding more reliable. 2. Observation System FUNCTION capture_web_state: TAKE a screenshot of the current page IDENTIFY interactive elements (buttons, links, inputs) MARK elements with numerical labels RETURN marked screenshot and element details Significance: Acts as the agent's sensory system. The marked elements help the agent understand what it can interact with, similar to how humans visually identify clickable elements on a page. 3. Action Framework DEFINE possible actions: - CLICK(element_id) - TYPE(element_id, text) - SCROLL(direction) - WAIT(duration) - BACK() - SEARCH() - ANSWER(result) Significance: Provides the agent's "physical" capabilities - what it can do on a webpage, like giving it hands to interact with the web interface. 4. Decision-Making System FUNCTION decide_next_action: INPUT: current_screenshot, element_list, task_description USE multimodal LLM to: ANALYZE visual and textual information REASON about next best action RETURN thought_process and action_command Significance: The brain of the operation. The LLM combines visual understanding with task requirements to decide what to do next. 5. Execution Loop WHILE task not complete: GET current web state DECIDE next action IF action is ANSWER: RETURN result EXECUTE action HANDLE any errors UPDATE context history Significance: Orchestrates the entire process, maintaining a continuous cycle of observation, decision, and action - similar to how humans navigate websites. Why This Architecture Works The potential of web agent architecture lies in its human-like approach to web navigation. Combining visual understanding with text processing navigates websites much like a person would - scanning the page, identifying interactive elements, and making informed decisions about what to click or type. This natural interaction style makes it particularly effective at handling real-world websites. Figure: Example workflow of Web Agents using images (Source) Natural Interaction Mimics human web browsing behavior Combines visual and textual understanding It makes decisions based on what it actually "sees" Robustness Can handle dynamic web content Adapts to different website layouts Recovers from errors and unexpected states Extensibility Easy to add new capabilities It can be enhanced with more advanced models Adaptable to different types of web tasks This architecture provides a foundation for building capable web agents, balancing the power of AI with structured web automation. As models and tools evolve, we can expect these agents to become even more sophisticated and reliable. Integrating Encord into Your Workflow Encord is a comprehensive data development platform designed to seamlessly integrate into your existing workflows, enhancing the efficiency and effectiveness of training data preparation for Web Agents and LLMs. Accuracy Encord's platform offers best-in-class labeling tools that enable precise and consistent annotations, ensuring your training data is accurately labeled. This precision directly contributes to the improved decision-making capabilities of your models. Contextuality With support for multimodal annotation, Encord allows you to label data across various formats—including images, videos, audio, and text—adding depth and relevance to your datasets. This comprehensive approach ensures that your models are trained with context-rich data, enhancing their performance in real-world applications. Scalability Encord's platform is built to scale efficiently with increasing data volumes, accommodating the growth needs of businesses. Encord ensures seamless integration and management of large datasets by leveraging cloud infrastructure without compromising performance. This scalability is supported by best practices outlined in Encord's documentation, enabling organizations to expand their AI initiatives confidently. Integrating Encord into your workflow allows you to streamline and expedite training data preparation, ensuring it meets the highest accuracy, contextuality, and scalability standards. This integration simplifies the data preparation process and enhances the overall performance of your Web Agents and LLMs, positioning your business for success in the competitive AI landscape. Automate your data pipelines with Encord Agents to reduce the time taken to achieve high-quality data annotation at scale. Conclusion Integrating web agents and Large Language Models (LLMs) has become a pivotal strategy for businesses aiming to thrive in today's data-driven economy. This synergy enables the efficient extraction, interpretation, and utilization of real-time web data, providing organizations with actionable insights and a competitive edge. Encord's platform plays a crucial role in this ecosystem by streamlining the training data preparation process. It ensures that data is accurate, contextually rich, and scalable, which is essential for developing robust LLM-driven solutions. Encord accelerates AI development cycles and enhances model performance by simplifying data management, curation, and annotation. To fully leverage the potential of advanced web agents and LLM integrations, we encourage you to explore Encord's offerings. Take the next step in optimizing your AI initiatives: Try Encord: Experience how Encord can transform your data preparation workflows. Streamline Your Data Preparation: Learn more about how Encord's tools can enhance your data pipeline efficiency.   By embracing these solutions, your organization can harness the full power of AI, driving innovation and maintaining a competitive advantage in the rapidly evolving digital landscape.

That’s a wrap for 2024, and what an amazing journey it has been helping our customers extract and use meaningful business context from their unstructured data in the easiest way possible. At Encord, we strive to be the last AI data platform teams will need to efficiently discover and prepare high-quality, relevant private datasets for training and fine-tuning AI models at scale.   Encord customers are pushing the boundaries on how AI can help improve business operations, save lives, delight users and customers, and, most importantly, make GenAI and custom models work better for businesses with richer data. All this while being maniacal about our customer experiences and building a lasting AI company.  This year we’ve: Helped customers like Synthesia and Flawless AI achieve groundbreaking GenAI research. Onboarded AI innovators like  Showed the world that multimodal is possible in a unified AI data platform while releasing  ___ game-changing and foundational product enhancements, including support for SAM 2 within 48 hrs of its public release.  Closed our $32M Series B to further support R&D and GTM Opened our San Francisco office to build and scale our global GTM functions. In addition to delighting our customers, in 2024, we evolved our industry-leading computer vision and medical AI data platform to enable teams to easily discover, manage, curate, and annotate petabyte scale document, text, and audio datasets. We also introduced a multimodal annotation interface facilitating reinforced learning from human feedback (RLHF) workflows and multi-file analysis and annotation in one view. Teams can now view video, audio, text and DICOM files in one interface to seamlessly orchestrate multimodal data workflows, fully customizable for any use case or project. What does this all mean, we are finishing 2024 as the only end-to-end AI data platform for multimodal data. Teams building AI systems for Computer Vision, Predictive, Generative, Conversational, and Physical AI can now also use Encord to efficiently transform petabytes of unstructured multimodal data into high-quality, representative datasets for training, fine-tuning and aligning AI models. Let's recap the highlights that our customers loved most.  Audio Encord’s audio data curation and annotation capability is specifically designed to enable effective annotation workflows for AI teams working with any type and size of audio dataset, literally any size. Teams can accurately classify multiple attributes within the same audio file with extreme precision down to the millisecond using customizable hotkeys or the intuitive user interface.  Whether you are building models for speech recognition, sound classification, or sentiment analysis for your contact center workflows, Encord provides a flexible, user-friendly platform to accommodate any audio and multimodal AI project regardless of complexity or size. Documents and Text AI Teams can use Encord for any annotation use case to comprehensively and accurately label large-scale document and text datasets, including: Named Entity Recognition (NER), Sentiment Analysis, Text Classification, Translation, Summarization, and RLHF.  Comprehensive annotation and quality control capabilities include the following: Customizable hotkeys and intuitive text highlighting - speeds up annotation workflows. Pagination navigation - whole documents can be viewed and annotated in a single task interface allowing for seamless navigation between pages for analysis and labeling. Flexible bounding box tools - teams can annotate multimodal content such as images, graphs and other information types within a document using bounding boxes. Free-form text labels - flexible commenting functionality to annotate keywords and text and the ability to add general comments. Multimodal Annotation Using the customizable multimodal annotation interface, teams can now view, analyze, and annotate multimodal files in one interface.  This unlocks a variety of cases that previously were only possible through cumbersome workarounds, including: Analyzing PDF reports alongside images, videos, or DICOM files to improve the accuracy and efficiency of annotation workflows by empowering labelers with extreme context.   Orchestrating RLHF workflows to compare and rank GenAI model outputs such as video, audio, and text content.   Annotate multiple videos or images showing different views of the same event.  Encord customers have already saved hours by eliminating the process of manually stitching video and image data together for same-scenario analysis. Instead, they now use Encord’s multimodal annotation interface to automatically achieve the correct layout required for multi-file annotation in one view. Data Agents Earlier this year, we also released Encord Data Agents, which enable teams to integrate AI models into their data workflows in a highly customizable way. Teams have integrated their own or foundation models, such as OpenAI’s GPT-4o and Anthropic’s Claude 3 Opus, to pre-label large datasets and smart-routing within data workflows and auto-reviews.   Using Encord Agents, teams are saving __ annotation time, boosting label throughput, and finding more label errors per expert review hour through agent integrations of both foundation models and in-house models. Teams can use the Encord Agents Library, a powerful yet flexible and lightweight framework that abstracts all the details of platform integration to integrate models into data workflows even faster.  The Encord Agents Library enables: Seamless access to the data and labels you need in a simple, accessible API.   Shorter time-to-value, allowing you to build and run Agents in a matter of minutes instead of hours. With APIs for Editor and Task Agents and one-line CLI test commands, you can prototype, build, and integrate cutting-edge models into your workflows easier than ever. SAM 2 for Accelerated Data Annotation Meta released Segment Anything Model 2 in July, and within 48 hrs of its release, Encord customers were able to leverage SAM 2 natively within the Encord platform to improve and accelerate mask prediction and object segmentation in image and video data.  Our customers have used the model millions of times to automate their labeling processes and have seen huge benefits of 6x faster performance compared to the original SAM model. Accessing SAM 2 capabilities natively in Encord has also saved AI teams hours of time and manual effort by eliminating the need to label individual frames of video for complex object masking.  Data Curation and Management Over the past few years, we have been working with some of the world’s leading AI teams at Synthesia, Philips, and Tractable to provide world-class infrastructure for data-centric AI development.  In conversations with many of our customers, we discovered a common pattern: teams have petabytes of data scattered across multiple cloud and on-premise data storages, leading to poor data management and curation.  Enter Encord Index. Index enables AI teams to unify massive datasets across countless distributed sources to securely discover, manage, and visualize billions of data files on one platform. By simply connecting cloud or on-prem data stores via our API or using our SDK, teams can instantly manage and visualize all of their unstructured data on Index. This view is dynamic and includes any new data that organizations accumulate following initial setup.  Teams can use granular data exploration functionality within to discover, visualize, and organize the full spectrum of real-world business data and a range of edge cases: Embeddings plots to visualize and understand large-scale datasets in seconds and curate the right data for downstream data workflows. Automatic error detection helps surface duplicates or corrupt files to automate data cleansing.   Powerful natural language search capabilities empower data teams to automatically find the right data in seconds, eliminating the need to manually sort through folders of irrelevant data.  Metadata filtering allows teams to find the data that they already know will be the most valuable addition to your datasets. As a result, our customers have achieved, on average, a 35% reduction in dataset size by curating the best data, seen upwards of 20% improvement in model performance, and saved hundreds of thousands of dollars in compute and human annotation costs.  We’re just getting started Encord is designed to enable teams to future-proof their data pipelines for growth in any direction—whether they are advancing laterally from unimodal to multimodal model development or looking for a secure platform to handle rapidly evolving datasets at petabyte scale.  Encord unites AI, data science, machine learning, and data engineering teams with a consolidated platform to search, curate, and label unstructured data, including images, videos, audio files, documents, and DICOM files, into the high-quality data needed to deliver improved model performance and production AI models faster. Our customers' focus on democratizing AI across businesses everywhere, paired with our relentless drive to delight our customers with magical product experiences, is the perfect foundation for an even more exciting 2025! 

Around 80% of information consists of unstructured data, including PDF documents and text files. The increasing data volume requires optimal tools and techniques for efficient document management and operational efficiency. However, extracting text from PDFs is challenging due to different document layouts, structures, and languages. In particular, data extraction from scanned PDF images requires more sophisticated methods, as the text in such documents is not searchable. PDF Optical Character Recognition (OCR) technology is one popular solution for quickly parsing the contents of scanned documents. It allows users to implement robust extraction pipelines with artificial intelligence (AI) to boost accuracy. In this post, we will discuss OCR, its benefits, types, workings, use cases, challenges, and how Encord can help streamline OCR workflows.  What is OCR? Optical Character Recognition (OCR) is a technology that converts text from scanned documents or images into machine-readable and editable formats. It analyzes character patterns and transforms them into editable text. The technique makes the document’s or image’s contents accessible for search, analysis, and integration with other workflows. Users can leverage OCR’s capabilities to digitize and preserve physical records, enhance searchability, and automate data extraction. It optimizes operations in multiple industries, such as legal, healthcare, and finance, by boosting productivity, reducing manual labor, and supporting digital transformation. What Does OCR Mean for PDFs? OCR technology helps transform image-based or scanned PDF documents into machine-readable and searchable PDF files. PDFs created through scanning often store content as static images, preventing users from editing or searching within these documents. OCR recognizes the characters in these scanned images and converts them into selectable text. The feature lets users edit PDF text, perform keyword searches, and simplify data retrieval using any PDF tool. For businesses and researchers, OCR-integrated PDFs streamline workflows, improve accessibility, and facilitate compliance with digital documentation standards. It also means that OCR tools are critical to modern document management and archiving. They allow organizations to extract text from critical files intelligently and derive valuable insights for strategic decision-making. Benefits of OCR As organizations increasingly rely on scanned PDFs to store critical information, the demand for OCR processes to make PDF text searchable will continue to grow. Below are some key advantages businesses can unlock by integrating PDF OCR software into their operations. Better Searchability: OCR converts scanned or image-based PDFs into searchable text, allowing users to locate specific information instantly with standard PDF readers. This capability is especially useful for large document repositories. Faster Data Extraction and Analysis: OCR automates information retrieval from unstructured documents, enabling quick extraction of critical data such as names, dates, and figures. This facilitates real-time analysis and integration with decision-making tools. Cost Savings: Automating document digitization and processing reduces the need for manual data entry and storage of physical files. This minimizes labor costs and increases profitability. High Conversion Accuracy and Precision: Converting scanned PDFs directly into Word documents or PowerPoint presentations often leads to errors and misaligned structures. With OCR-powered tools, users can efficiently convert searchable PDFs into their desired formats with PDF converters, ensuring accuracy and precision in the output. Legal and Regulatory Compliance: Digitized and organized documents help organizations meet compliance requirements. OCR ensures fast retrieval of records during audits and legal inquiries. Scalability: Whether processing hundreds or millions of documents, OCR scales effortlessly to handle enterprise-level demands. Integrability with AI Systems: OCR-generated data can feed into AI models for natural language processing, analytics, and automation. The functionality enhances broader business intelligence capabilities and customer experience. How Does OCR Work? OCR comprises multiple stages to convert scanned or image-based PDFs into machine-readable text. Here's a breakdown of the process: Image Acquisition The process begins with acquiring a digital image of the document through scanning, photography, or capturing an image from a PDF. The image can be in a standard JPG or PNG format. The quality and resolution of this image are critical for accurate OCR performance. Preprocessing Preprocessing improves image quality for better text recognition. Common techniques include: Noise Removal: Eliminating specks, smudges, or background patterns. Deskewing: Correcting tilted or misaligned text. Binarization: Converting the image into a binary format (black and white) for easier character recognition. Contrast Enhancement: Adjusting brightness and contrast for clear text. Text Recognition This is the core phase of OCR and uses three key techniques: Pattern Matching: Comparing detected shapes with stored templates of known characters. Feature Extraction: Identifying features like curves, lines, and intersections to decode characters. Layout Recognition: Analyzing the document structure, including columns, tables, and paragraphs, to retain the original formatting. Post Processing Postprocessing refines the output by correcting errors using language models or dictionaries and ensuring proper formatting. This step often includes spell-checking, layout adjustments, and exporting to desired formats like Word or Excel. It may require using PDF editors like Adobe Acrobat to adjust inconsistencies in the converted files. Types of OCR OCR technology caters to diverse use cases, leading to different types of OCR systems based on functionality and complexity. The sections below highlight four OCR types. Simple OCR Simple OCR uses basic pattern-matching techniques to recognize text in scanned images and convert them into editable digital formats. Simple OCR While effective for clean, well-structured file formats, it struggles with complex layouts, handwriting, or stylized fonts. It is ideal for straightforward text conversion tasks like digitizing printed books or reports. Intelligent Character Recognition (ICR) ICR is an advanced form of OCR designed to recognize handwritten characters. It uses machine learning (ML) and neural networks to adapt to different handwriting styles, providing higher accuracy. ICR detecting the word “Handwriting” It helps process forms, checks, and handwritten applications. However, accuracy may still vary depending on handwriting quality and file size. Optical Mark Recognition (OMR) OMR identifies marks or symbols on predefined forms, such as bubbles or checkboxes. It helps in applications like grading tests, surveys, and election ballots.  OMR Scanner recognizing marked checkboxes OMR requires structured forms with precise alignment and predefined layouts for accurate detection. Intelligent Word Recognition (IWR) Intelligent Word Recognition (IWR) identifies entire words as cohesive units rather than breaking them down into individual characters. This approach makes it particularly effective for processing cursive handwriting and variable fonts. IWR Recognizing Cursive Handwriting Unlike Intelligent Character Recognition (ICR), which focuses on recognizing characters one at a time, IWR analyzes the complete word image in a single step. The approach enables faster and more context-aware recognition. It is helpful in scenarios where context-based recognition is essential, such as signature verification or handwritten document digitization. OCR Use Cases OCR's versatility and cost-effectiveness drive its rapid adoption across various industries as businesses use it to streamline everyday operations. The list below showcases some of the most prominent OCR applications in key sectors today. Legal and Finance OCR refines knowledge management in legal and financial sectors by digitizing critical documents. It automates contract analysis, extracting clauses, dates, and terms for faster review. In addition, the technology simplifies invoice processing in finance. It captures data like amounts and vendor details for seamless accounting. It also enables e-discovery in legal cases by making scanned documents searchable. The technique ensures compliance by organizing records for quick retrieval during audits. Healthcare The healthcare industry improves document management with OCR by digitizing patient records, prescriptions, and insurance claims for quick retrieval and processing. It enables accurate extraction of critical data from medical forms, speeding up billing processes and reducing errors. OCR also aids in converting historical records into searchable digital formats. The approach enhances research efforts by allowing professionals to manage large volumes of healthcare documentation. Education Teachers and students can use OCR to digitize textbooks, lecture notes, and research materials to make them searchable and easily accessible. OCR also helps in administrative tasks like processing student applications and transcripts. It allows instructors to preserve historical documents and convert them into digital editable formats. Moreover, OCR enhances study material accessibility by transforming them into formats suitable for students from different backgrounds. For example, teachers can integrate OCR with AI-powered translation software. They can use it to translate scanned PDF documents in French and German into English or other local languages, allowing for multilingual learning. Government and Public Sector OCR improves government and public sector operations by digitizing records, including birth certificates, tax forms, and land registries, for quick access and retrieval. It automates data extraction from citizen applications and forms, reducing manual workloads. OCR also supports transparency by making public documents searchable and accessible through official government websites. Retail and E-Commerce OCR contributes to retail and e-commerce by automating invoice processing, inventory management, and order tracking. It extracts key product details from receipts and invoices, ensuring accuracy and relevance in accounting procedures. OCR also enables quick integration of scanned product labels and packaging data into digital systems. This allows retailers to use the data for better catalog management and sales tracking. Additionally, it supports customer service by converting forms, feedback, and returns into searchable and manageable digital formats. Logistics OCR improves logistics efficiency by automating data extraction from shipping labels, invoices, and customs documents. It optimizes inventory management and tracking by converting physical records into digital formats. The method also speeds up delivery forms and bills of lading processes, reducing manual data entry. This enhances accuracy, boosts operational efficiency, and supports real-time tracking across the supply chain. Media and Publishing In media and publishing, OCR transforms printed materials like newspapers, books, and magazines into searchable and accessible digital formats. It simplifies content archiving, allowing users to retrieve articles and historical publications quickly. The technology also aids in converting manuscripts into digital formats for editing and publishing. Efficiently indexing large volumes of content helps improve the speed and accuracy of editorial workflows. Travel and Transportation The travel and transportation industry uses OCR to automate data extraction from documents like boarding passes, tickets, and passports, enhancing check-in efficiency and reducing errors. It simplifies booking and reservation systems by converting paper forms into digital formats. Additionally, OCR improves transportation management by digitizing vehicle records, driver licenses, and shipping documents. This improves accuracy, efficiency, and overall customer service. Learn how to label text in our complete guide to text annotation OCR Challenges Despite its many advantages, OCR technology faces several challenges that can limit its effectiveness in specific applications. These include: Accuracy: OCR accuracy heavily depends on the quality of input documents. Poor scan resolution, faded text, and noisy backgrounds often lead to recognition errors and reduce output reliability. Language Diversity: OCR systems may struggle to support multiple languages, especially those with complex scripts or right-to-left text orientation. While advanced tools address this, lesser-used languages often face limited support. Document Structure: OCR struggles with maintaining the formatting and layout of complex documents containing tables, columns, or graphics. This can result in misaligned or missing content, especially in documents with intricate designs. Computational Resources: High-quality OCR processing requires significant computational resources, particularly for large volumes or complex layouts. This can pose challenges for organizations with limited technical infrastructure. Lacks Contextual and Semantic Understanding: While OCR excels at recognizing characters, it cannot interpret context or semantics. This limitation affects tasks requiring comprehension, such as extracting meaning from ambiguous text or interpreting handwriting nuances. Data Security and Privacy: Processing sensitive documents with OCR, especially on cloud-based platforms, raises privacy and compliance concerns. Ensuring secure processing environments is critical for protecting sensitive information. Encord for Converting PDF with OCR The challenges mentioned above can hamper a user’s ability to leverage OCR’s capabilities to get a clean and accurate editable PDF. Although multiple online tools offer OCR functionality, they can fall short of the features required for building scalable PDF text extraction systems. Alternatively, enterprises can build customized solutions using open-source libraries for specific use cases. However, the development may require significant programming and engineering expertise to create a robust and secure document management platform. As industries embrace greater digitization, organizations must invest in more integrated solutions that combine advanced OCR capabilities with AI-driven functionality. One such option is Encord, an end-to-end AI-based data curation, annotation, and validation platform with advanced OCR features. Encord can help you build intelligent extraction pipelines to analyze textual data from any document type, including scanned PDFs. It is compatible with Windows, Mac, and Linux. Encord Key Features Document Conversion: Encord lets you quickly convert scanned PDFs into editable documents through OCR. You can easily adjust the converted files further using tools like Acrobat Pro, Google Docs, or Microsoft Word. Curate Large Datasets: It helps you curate and explore large volumes of text through metadata-based granular filtering and natural language search features. Encord can handle various document types and organize them according to their contents. The ability leads to better contextual understanding when parsing text from image-based PDFs. Multimodal Support: Encord is a fully integrated multimodal framework that can help you integrate text recognition pipelines with other modalities, such as audio, images, videos, and DICOM. This will help you convert PDFs with complex layouts and visuals more accurately. Data Security: The platform complies 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 protect data privacy. 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. 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. PDF OCR: Key Takeaways Businesses are transforming OCR from a standalone tool for converting scanned images into text and turning them into a key component of AI-driven applications. They now use OCR to extract text and build scalable solutions for natural language processing (NLP) and generative AI frameworks. Below are a few key points regarding OCR: OCR and PDFs: Users leverage OCR to convert scanned PDF images into searchable documents. The functionality helps them optimize document management and analyze textual data for more insights. OCR Challenges: Poor image quality and different layouts, structures, and contextual design make it difficult for OCRs to read text from scanned PDFs accurately. Encord for OCR: Encord’s powerful AI-based data extraction and state-of-the-art (SOTA) OCR features can help you analyze complex image-based PDFs instantly.

Audio classification is revolutionizing the way machines understand sound, from identifying emotions in customer service calls to detecting urban noise patterns or classifying music genres. By combining machine learning with detailed audio annotation techniques, AI systems can interpret and label sounds with remarkable precision. This article explores how audio data is transformed through annotation, the techniques and tools that make it possible, and the real-world applications driving innovation. If you've ever wondered how AI distinguishes between a dog bark and a car horn—or how it knows when you're happy or frustrated—read on to uncover process behind audio classification. What is Audio Classification? Audio classification in the context of Artificial Intelligence (AI) refers to the use of machine learning and related computational techniques to automatically categorize or label audio recordings based on its content. Instead of having a human listen to an audio clip and describe what it is (e.g. whether it’s a musical piece, a spoken sentence, a bird call, or an ambient noise) an AI system attempts to identify patterns within the sound signal and assign one or more meaningful labels accordingly. Audio Classification (Source) Audio classification can be done by annotating audio files to train machine learning models. Audio annotation is the process of adding meaningful labels to raw audio data to prepare it for training ML models. Since audio data is complex and consists of various sound signals, speech, and sometimes noise, it needs to be broken down into smaller, structured segments for effective learning. These labeled segments serve as training data for machine learning or deep learning models, enabling them to recognize patterns and make accurate predictions. Audio Data Annotation (Source) For example, imagine a recording with two people talking. To classify this audio file into meaningful categories, it needs to be annotated first. During the annotation process, the speech of each person can be marked with a label such as "Speaker A" and "Speaker B" along with precise timestamps indicating when each speaker starts and stops talking. This technique is known as speaker diarization, where each speaker's contributions are identified and labeled. Additionally, the emotional tone of the speakers, such as "Happy" or "Angry," can be annotated for models that detect emotions, such as those used in emotion recognition systems. By doing this, the annotated data provides the machine learning model with clear information about: Who is speaking (speaker identification). The time frame of the speech. The nature of the speech or sound (emotion, sentiment, or event). The annotated data is then fed into the machine learning pipeline, where the model learns to identify specific features within the audio signals. Audio annotation bridges the gap between raw audio and AI models. By providing labeled examples of speech, emotions, sounds, or events, it allows machine learning models to classify audio files accurately. Whether it is recognizing speakers, understanding emotions, or detecting background events, annotation ensures that the machine understands the content of the audio in a structured way, enabling it to make intelligent decisions when exposed to new data. Types of Audio Annotations for AI Audio annotation is an important process in developing AI systems that can process and interpret audio data. By annotating audio data, AI models can be trained to recognize and respond to various auditory elements. Different types of audio annotations help capture various features and structures of audio data. Here are the main types of audio annotations used for audio classification. Below are detailed explanations of key types of audio annotations: Label Annotation Label annotation refers to assigning a single label to an entire audio file or segment to classify the type of sound.This annotation is helpful in building AI systems to classify environmental sounds like "dog bark," "car horn," or "rain." Example: Audio Clip: Recording of rain. Label: "Rain." Timestamp Annotation Timestamp annotation refers to marking specific time intervals where particular sounds occur in an audio file. This annotation is helpful in building AI systems to detect when specific events (e.g., "baby crying") happen in a long audio recording. Example: Audio Clip: Audio file with multiple sounds. Annotations: 00:03–00:06: "Baby crying" 00:09–00:13: "Dog barking" Segment Annotation Segment annotation refers to dividing an audio file into segments, each labeled with the predominant sound or event. This annotation is helpful in building AI systems to identify different types of sounds in a podcast or meeting recording. Example: Audio Clip: A podcast excerpt. Segments: 00:00–00:10: "Intro music" 00:12–00:20: "Speech" 00:23–00:: "Background noise" Phoneme Annotation Phoneme annotation refers to labeling specific phonemes (smallest units of sound) within an audio file. This may be helpful in building AI systems for speech recognition or accent analysis. Example: Audio Clip: The spoken word "cat." Annotations: 00:00–00:05: /k/ 00:05–00:10: /æ/ 00:10–00:15: /t/ Event Annotation Event annotation refers to annotating discrete audio events that may overlap or occur simultaneously. This annotation is useful in building AI systems for urban sound classification to detect overlapping events like "siren" and "car horn." Example: Audio Clip: Urban sound. Annotations: 00:05–00:10: "Car horn" 00:15–00:20: "Siren" Speaker Annotation Speaker Annotation refers to identifying and labeling individual speakers in a multi-speaker audio file. This annotation is useful in building AI systems for speaker diarization in meetings or conversations. Example: Audio Clip: A user conversation. Annotations: 00:00–00:08: "Speaker 1" 00:08–00:15: "Speaker 2" 00:15–00:20: "Speaker 1" Sentiment or Emotion Annotation Sentiment or Emotion Annotation refers to labeling audio segments with the sentiment or emotion conveyed (e.g., happiness, sadness, anger). This annotation is useful in building systems for emotion recognition in customer service calls. Example: Audio Clip: Audio from a call center. Annotations: 00:00–00:05: "Happy" 00:05–00:10: "Neutral" 00:10–00:15: "Sad" Language Annotation Language annotation refers to identifying the language spoken in an audio file or segment. This annotation is useful in building systems for multilingual speech recognition or translation tasks. Example: Audio Clip: Audio with different languages. Annotations: 00:00–00:15: "English" 00:15–00:30: "Spanish" Noise Annotation Noise annotation refers to labeling background noise or specific types of noise in an audio file. This may be used in noise suppression or enhancement in audio processing. Example: Audio Clip: Audio file with background noise. Annotations: 00:00–00:07: "White noise" 00:07–00:15: "Crowd chatter" 00:15–00:20: “Traffic noise 00:20–00:25: "Bird chirping" Explore the top 9 audio annotation tools in the industry. Why Annotate Audio Files Using Encord? Encord’s audio annotation capabilities are designed to assist the annotation process for users or teams working with diverse audio datasets. The platform supports various audio formats, including .mp3, .wav, .flac, and .eac3, facilitating seamless integration with existing data workflows.  Flexible Audio Classification Encord's audio annotation tool allows users to classify multiple attributes within a single audio file with millisecond precision. This flexibility supports various use cases, including speech recognition, emotion detection, and sound event classification. The platform accommodates overlapping annotations, enabling the labeling of concurrent audio events or multiple speakers. Customizable hotkeys and an intuitive interface enhance the efficiency of the annotation process. Advanced Annotation Capabilities Encord integrates with SOTA models like OpenAI's Whisper and Google's AudioLM to automate audio transcription. These models provide highly accurate speech-to-text capabilities, allowing Encord to generate baseline annotations for audio data. Pre-labeling simplifies the annotator's task by identifying key elements such as spoken words, pauses, and speaker identities, reducing manual effort and increasing annotation speed. Seamless Data Management and Integration Encord supports various audio formats, including .mp3, .wav, .flac, and .eac3. This helps in integrating audio datasets with existing data workflows. Users can import audio files from cloud storage services like AWS, GCP, Azure, or OTC, and organize large-scale audio datasets efficiently. The platform also offers tools to assess data quality metrics, ensuring that only high-quality data is used for AI model training. Collaborative Annotation Environment For teams working on large-scale audio projects, Encord provides unified collaboration features. Multiple annotators and reviewers can work simultaneously on the same project, facilitating a smoother, more coordinated workflow. The platform's interface enables users to track changes and progress, reducing the likelihood of errors or duplicated efforts. Quality Assurance and Validation Encord’s AI-assisted quality assurance tools compare model-generated annotations with human reviews(HITL), identifying discrepancies and providing recommendations for corrections. This dual-layer validation system ensures annotations meet the high standards required for training robust AI models. Integration with Machine Learning Workflows Encord platform is designed to integrate easily with machine learning workflows. Its comprehensive label editor offers a complete solution for annotating a wide range of audio data types and use cases. It supports annotation teams in developing high-quality models. How to Annotate Audio Files Using Encord? To annotate audio files in Encord, you can follow these steps: Step 1: Navigate to the queue tab  Navigate to the Queue tab of your Project and select the audio file you want to label. Step 2: Select annotation type For audio files, you can use two types of annotations: Audio Region objects: Select an Audio Region class from the left side menu. Click and drag your cursor along the waveform to apply the label between the desired start and end points. Apply any attributes to the region if required. Repeat for as many regions as necessary. Classifications: Select the Classification from the left side menu. For radio buttons and checklists, select the value(s) you want the classification to have. For text classifications, enter the desired text. Step 3: Save your labels  Save your labels by clicking the Save icon on the editor header. Important to note: It's important to note that only Audio Region objects and classifications are supported for audio files. Regular object labels (like bounding boxes or polygons) are not available for audio annotation. For more detailed information on audio annotation, you can refer to the How to Label documentation. Use Case Examples of Audio Classification Encord offers advanced audio annotation capabilities that facilitate the development of multimodal AI models. Here are the three key features supported by Encord: Speaker Recognition Speaker recognition involves identifying and distinguishing between different speakers within an audio file. Encord's platform enables precise temporal classifications, allowing annotators to label specific time segments corresponding to individual speakers. This is essential for training AI models in applications like transcription services, virtual assistants, and security systems. Example: Imagine developing an AI system for transcribing and identifying speakers during a multi-participant virtual meeting or call. Annotators can use Encord to label specific sections of an audio file where individual speakers are talking. For example, the orange-highlighted segment represents Speaker A, speaking between 00:06.14 and 00:14.93, with an emotion tag labeled as Happy. The purple-highlighted segment identifies Speaker B, who begins speaking immediately after Speaker A. Speaker Recognition (Source) These annotations enable the AI model to learn: Speaker Identification: Accurately recognize and attribute each spoken segment to the correct speaker, even in overlapping or sequential dialogues. Emotion Recognition: Understand emotional tones within speech, such as happiness, sadness, or anger, which can be particularly useful for sentiment analysis. Speech Segmentation: Divide an audio file into distinct time frames corresponding to individual speakers to improve transcription accuracy. For instance, in a customer support call, the AI can distinguish between the representative (Speaker A) and the customer (Speaker B), automatically tagging emotions like "Happy" or "Frustrated." This capability allows businesses to analyze conversations, monitor performance, and understand customer sentiment at scale. By providing precise speaker-specific annotations and emotional classifications, Encord ensures that AI models can identify, segment, and analyze speakers with high accuracy, supporting applications in transcription services, virtual assistants, and emotion-aware AI systems. Sound Event Detection Sound event detection focuses on identifying and classifying specific sounds within an audio file, such as alarms, footsteps, or background noises. Encord's temporal classification feature allows annotators to mark the exact time frames where these sound events occur, providing precise data for training models in surveillance, environmental monitoring, and multimedia indexing. Example: Imagine developing an AI system for weather monitoring that identifies specific weather sounds from environmental audio recordings. Annotators can use Encord to label occurrences of sounds such as thunder, rain, and wind within the audio. For instance, as shown in the example, the sound of thunder is highlighted and labeled precisely with timestamps (00:06.14 to 00:14.93). These annotations enable the AI model to accurately recognize thunder events, distinguishing them from other sounds like rain or wind. Sound Event Detection (Source) With these well-annotated audio segments, the AI system can: Monitor Weather Conditions: Automatically detect thunder in real-time, triggering alerts for potential storms. Improve Weather Forecasting Models: Train AI models to analyze sound events and predict extreme weather patterns. Support Smart Devices: Enable smart home systems to respond to weather events, such as closing windows when rain or thunder is detected. By providing precise, timestamped annotations for weather sounds, Encord ensures the AI model learns to identify and differentiate between environmental sound events effectively. Audio File Classification Audio file classification entails categorizing entire audio files based on their content, such as music genres, podcast topics, or environmental sounds. Encord supports global classifications, allowing annotators to assign overarching labels to audio files, streamlining the organization and retrieval of audio data for various applications. Imagine developing an AI system for classifying environmental sounds to improve applications like smart audio detection or media organization. Annotators can use Encord to globally classify audio files based on their dominant context. In this example, the entire audio file is labeled as "Environment: Cafe" with a global classification tag.  The audio file spans a full duration of 00:00.00 to 13:45.13, and the annotator has assigned a single global label, "Cafe", under the Environment category. This classification indicates that the entire file contains ambient sounds typically heard in a café, such as background chatter, clinking of cups, and distant music. Audio File Classification (Source) Suppose you are building an AI-powered sound classification system for multimedia indexing: The AI can use global annotations like "Cafe" to organize large audio datasets by environment types, such as Park, Office, or Street. This labeling enables media platforms to automatically categorize and tag audio clips, making them easier to retrieve for specific use cases like virtual reality simulations, environmental sound recognition, or audio-based content searches. For applications in smart devices, an AI model can learn to recognize "Cafe" sounds to optimize noise cancellation or recommend ambient soundscapes for users. By providing precise global classifications for audio files, Encord ensures that AI systems can quickly analyze, organize, and act on sound-based data, improving their efficiency in real-world applications. Best Practices for Categorizing and Annotating Audio Below are best practices for categorizing and annotating audio files, organized into key focus areas that ensure a reliable, effective, and scalable annotation process. Consistency in Labels This refers to ensuring that every annotator applies the same definitions and criteria when labeling audio. Consistency is achieved through well-defined categories, clear guidelines, thorough training, and frequent checks to ensure everyone interprets labels the same way. As a result, the dataset remains uniform and reliable, improving the quality of any analysis or model training done on it. Team Collaboration This involves setting up effective communication and coordination among all individuals involved in the annotation process. By having dedicated communication channels, Q&A sessions, and peer review activities, the annotating team can quickly resolve uncertainties, share knowledge, and maintain a common understanding of the labeling rules, leading to more accurate and efficient work. Quality Assurance Quality assurance (QA) ensures the accuracy, reliability, and consistency of the annotation work. QA includes conducting spot checks on randomly selected samples, and continuously refining the guidelines based on feedback and identified errors. Effective QA keeps the labeling process on track and gradually improves its overall quality over time. Handling Edge Cases Edge cases are unusual or ambiguous audio samples that don’t fit neatly into predefined categories. Handling them involves having a strategy in place (such as providing an “uncertain” label) and allowing annotators to leave notes, and updating the taxonomy as new or unexpected types of sounds appear. This ensures that the annotation task remains flexible and adaptive. Key Takeaways: Audio File Classification Audio classification uses AI to categorize audio files into meaningful labels, enabling applications like speaker recognition, emotion detection, and sound event classification. Handling noisy data, overlapping sounds, and diverse audio patterns can complicate annotation. Consistent labeling and precise segmentation are essential for success. Accurate annotations, including timestamps and labeled events, ensure robust datasets. These are key for training AI models that perform well in real-world scenarios. Encord streamlines annotation with support for diverse file formats, millisecond precision, collaborative workflows, and AI-assisted quality assurance. Consistency, collaboration, and automation tools enhance annotation efficiency, while strategies for edge cases improve dataset adaptability and accuracy.

What is Named Entity Recognition? Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP) that involves locating and classifying named entities mentioned in unstructured text into predefined categories such as names, organizations, locations, dates, quantities, percentages, and monetary values. NER serves as a foundational component in various NLP applications, including information extraction, question answering, machine translation, and sentiment analysis. At its core, NER processes textual data to identify and categorize key information. For example, in the sentence    "Apple is looking at buying U.K. startup for $1 billion." An NER system should recognize "Apple" as an Organization (ORG), "U.K." as a Geopolitical entity (GPE), and "$1 billion" as a Monetary value (MONEY). Named Entity Recognition (NER) Example How NER Works The NER process identifies and classifies key information (entities) in text into predefined categories such as names, organizations, locations, dates, and more. The following are the general steps of the NER process: Step #1: Text Input The process begins with raw text data that needs to be analyzed. "Apple Inc. is planning to open a new office in San Francisco in March 2025." Step #2: Text Preprocessing This step involves preparing the text for analysis by performing following operations. Tokenization Splitting the text into individual units called tokens (words, punctuation, etc.). ["Apple", "Inc.", "is", "planning", "to", "open", "a", "new", "office", "in", "San", "Francisco", "in", "March", "2025", "."] Part-of-Speech Tagging Assigning grammatical tags to each token to understand its role in the sentence. [("Apple", "NNP"), ("Inc.", "NNP"), ("is", "VBZ"), ("planning", "VBG"), ("to", "TO"), ("open", "VB"), ("a", "DT"), ("new", "JJ"), ("office", "NN"), ("in", "IN"), ("San", "NNP"), ("Francisco", "NNP"), ("in", "IN"), ("March", "NNP"), ("2025", "CD"), (".", ".")] Step #3: Feature Extraction Deriving relevant features from the tokens to assist the NER model in making accurate predictions. Contextual Features: Considering surrounding words to understand the context. Orthographic Features: Examining capitalization, punctuation, and numerical patterns. Lexical Features: Utilizing dictionaries or gazetteers to match known entity names. Step #4: Model Application Applying a trained NER model to classify each token (or group of tokens) into predefined entity categories. Machine Learning Models: Using algorithms like Conditional Random Fields (CRFs) or neural networks trained on annotated datasets. Rule-Based Systems: Employing handcrafted rules and patterns for specific entity types. Step #5: Entity Classification Assigning labels to tokens based on the model's predictions. [("Apple Inc.", "ORG"), ("San Francisco", "LOC"), ("March 2025", "DATE")] Step #6: Post-Processing Refining the output to handle nested entities, resolve ambiguities, and ensure consistency. It can determine the correct entity type when a token could belong to multiple categories. For example "Jordan" could refer to a person's name or a country; context is used to decide the correct classification. Or, identified nested entities (entities within entities), such as a person's name within an organization. For example "President [Barack Obama] of [the United States]" Step #7: Output Generation Producing the final annotated text with entities highlighted or in a structured format like JSON or XML. Labels and Tagging Schemes in NER Labels in NER In NER, labels are the categories assigned to words or phrases identified as named entities within a piece of text. These labels indicate the type of entity detected, such as a person, organization, location, or date. The labeling process allows unstructured text to be converted into structured data, which can be used for various applications like information retrieval, question answering, and data analysis. The set of labels used in NER can vary depending on the specific application, domain, or dataset. However, some standard labels are widely used across different NER systems: For example, in the following sentence: Bill Gates and Paul Allen founded Microsoft Bill Gates and Paul Allen recognized and classified as a PERSON entity and  Microsoft is classified as an ORG (organization). Tagging Schemes in NER In addition to the entity labels, NER systems often use tagging schemes to indicate the position of words within entities. The most common schemes are: BIO Tagging (Begin, Inside, Outside) Example IOBES Tagging (Inside, Outside, Begin, End, Single) Example IOB2 This tagging is similar to BIO but it ensures that the beginning of every entity is marked with a B- tag, even if it immediately follows another entity of the same type. Example: In this case, "Apple" is tagged as the beginning of an organization (B-ORG), and "U.K." is tagged as the beginning of a location (B-LOC). BIOES (Beginning, Inside, Outside, End, Single) It is another variation that includes the end and single tags for more precise boundary detection. Example: Here, both "Tesla" and "SolarCity" are single-token entities tagged as S-ORG. Domain-Specific Labels In specialized domains, additional labels may be used to capture domain-specific entities. For example in the biomedical domain, the labels such as Gene/Protein, Disease, Chemical, Drug are used. Similarly in financial domain labels such as Financial Instrument, Market Index, Economic Indicator etc. are used. Approaches of NER  Various approaches have been developed to annotate text for NER. Following are the popular approaches that are used. Rule-Based Methods Rule-based NER systems rely on manually specified linguistic rules and patterns to identify entities. These rules often utilize regular expressions, dictionaries (gazetteers), and part-of-speech tagging to detect predefined entity types. For example, a rule might specify that a capitalized word followed by "Inc." or "Ltd." should be classified as an organization. While rule-based methods can achieve high precision in specific domains, they often suffer from limited recall and are not easily scalable to diverse or evolving datasets. Additionally, developing and maintaining these rules can be labor-intensive and may not generalize well to new or informal text sources. Machine Learning-Based Methods Machine learning approaches involve training statistical models on annotated datasets to automatically recognize entities. Algorithms such as Conditional Random Fields (CRFs) and Support Vector Machines (SVMs) have been commonly used in this context. These models learn to identify entities based on features extracted from the text, such as word shapes, context words, and syntactic information. Machine learning methods generally offer better adaptability to different domains compared to rule-based systems and can handle a wider variety of entity types. However, they require substantial amounts of labeled training data and may still struggle with recognizing entities in noisy or informal text. Deep Learning-Based Methods Deep learning based methods use neural networks to capture complex patterns in data. Models such as Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Transformers (e.g., BERT) have been used to understand the text. These models can automatically learn feature representations from raw text, reducing the need for manual feature engineering. Deep learning-based NER systems have achieved state-of-the-art performance across various datasets and languages. However, it requires large amounts of training data and computational resources, and their performance can be sensitive to the quality of the data.  Hybrid Approaches Hybrid NER systems combine elements of rule-based, machine learning, and deep learning methods to use the advantages of each. For example, a hybrid system might use rule-based techniques to preprocess text and identify obvious entities, followed by a machine learning model to detect more complex cases. Alternatively, deep learning models can be supplemented with domain-specific rules to improve accuracy in specialized fields. Hybrid approaches aim to balance precision and recall while maintaining flexibility across different domains and text types. Each of these approaches has its own set of trade-offs concerning accuracy, scalability, and resource requirements. The choice of method often depends on the specific application, the availability of labeled data, and the computational resources at hand. Evaluation Metrics for NER Evaluating a NER model is essential to measure its ability to accurately identify and classify entities. The evaluation metrics typically focus on Precision, Recall, and F1-Score, which are calculated based on the comparison between the predicted entities and the actual entities in the dataset. Precision Precision measures the proportion of entities predicted by the model that are correct. High precision indicates that the model makes fewer false positive errors. Recall Recall measures the proportion of actual entities that are correctly identified by the model. High recall indicates that the model successfully captures most of the relevant entities. F1-Score The F1-Score is the harmonic mean of Precision and Recall, providing a single score that balances the two. High F1-Score suggests a good balance between precision and recall. Evaluating an NER Model Consider the following example: Apple Inc. is planning to open a new office in San Francisco in March 2025. Ground Truth (Actual Entities):   Model Prediction:   Calculation:   Metrics: Precision = TP/(TP + FP) = 2 / (2 + 1) = 0.67 Recall = TP / (TP + FN) =  2 / (2 + 1) = 0.67 F1-Score = 2 x (Precision x Recall / Precision + Recall) = 2 x (0.67 x 0.67 / 0.67 + 0.67) = 0.67 Tools for Transform data for NER Transforming data for NER involves converting raw text into a structured, annotated format suitable for model training. Various tools are available for this task, each offering unique features to facilitate the process. Below is a detailed explanation of tools that help transform data for NER: Encord Encord is an AI data development platform for managing, curating and annotating large-scale text and document datasets, as well as evaluating LLM performance.  AI teams can use Encord to label document and text files containing text and complex images and assess annotation quality using several metrics. The platform has robust cross-collaboration functionality across: Encord Index: Unify petabytes of unstructured data from multiple fragmented data sources to one platform for streamlined data management and curation. Index enables unparalleled visibility into very large document datasets using embeddings based natural language search and metadata filters, to enable teams to explore and curate the right data to be labeled and used for AI model training and fine-tuning. Encord Annotate: Leverage SOTA AI-assisted labeling workflows and flexibly setup complex ontologies to efficiently and accurately label largescale document and text datasets 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. NER annotation in Encord (Source) Doccano Doccano is an open-source, user-friendly annotation tool for text labeling tasks which also supports NER annotation. It has following features: Intuitive interface for labeling text spans. Support for sequence labeling (NER), text classification, and translation tasks. Collaborative annotation for teams. Export options for labeled data in formats like JSON, JSONL, or CSV, compatible with frameworks like spaCy. Prodigy Prodigy is a commercial, Python-based annotation tool designed for machine learning workflows and can be used for NER annotations. It has following features: Active learning to prioritize uncertain samples for annotation. Seamless integration with spaCy models. Support for manual annotation, model-in-the-loop annotation, and rule-based labeling. Flexible export formats for training data Snorkel Snorkel is a data programming platform for programmatically labeling and transforming training data. It supports many annotation tasks including support for NER annotation. It has following features: Create labeling functions to annotate data programmatically. Combines weak supervision signals to generate probabilistic labels. Scalable and suitable for large datasets. Snorkel NER annotation (Source) spaCy spaCy is a popular NLP library in Python. It also provides options for training and evaluating NER models. It has following features: Pre-trained models for entity recognition. Supports custom NER annotation and training pipelines. Integration with Prodigy for annotation tasks. spaCy NER example (Source)  OpenNLP Apache OpenNLP is a machine learning toolkit for processing natural language text. It also supports NER annotations. It has following features:  Pre-trained models for NER in multiple languages. Tools for training custom NER models using labeled data. Support for tokenization, sentence segmentation, and other preprocessing tasks. NER in OpenNLP (Source) Stanza Stanza is a Python NLP library developed by Stanford NLP Group. It supports multilingual NER and provides different NER models. It has following features: Pre-trained NER models for multiple languages. Easy integration with Python workflows. Stanza NER example (Source) Spark NLP Spark NLP is a scalable NLP library built on Apache Spark. It is suitable for distributed computing. It also provides the support for NER annotations. It has following features: Pre-trained NER models for large-scale text processing. Supports training custom models for NER tasks. Integration with other Spark-based tools. Spark NLP example (Source) How Encord helps in NER data annotation Encord supports various data types, including text, making it suitable for NER annotation tasks. It helps in managing, annotating, and iterating on training data for machine learning tasks. Here is how Encord helps in the NER annotation: Intuitive Annotation Interface Encord offers a user-friendly text annotation interface, making it easy for annotators to highlight and label text spans as entities. It helps in highlighting text directly to label it as an entity. Annotators can highlight specific words or phrases within the text. Annotators can assign entity labels, such as PERSON, LOCATION, ORGANIZATION, DATE, or any other custom tag defined in the ontology. Ontology Management Encord allows you to define a clear and structured ontology for your NER project. This ontology ensures consistent labeling and defines the entity types and their attributes. Users can create custom ontologies for specific projects or industries. This flexibility ensures that the annotation schema aligns with the requirements of domain-specific NER tasks.  Collaborative Annotation and Review Encord supports team-based annotation projects. It allows multiple annotators to work on the same dataset while maintaining consistency. It enables project managers or reviewers to check and approve annotations using built-in review workflows. It supports multi-stage review processes to help ensure high-quality labels. Model-Assisted Annotation Encord integrates with pre-trained models or custom machine learning (ML) models to assist annotators by providing pre-annotations. Annotators can validate, correct, or refine these predictions, significantly reducing manual workload. In Encord you can import a pre-trained NER model (e.g., spaCy, Hugging Face Transformers) and use the model to generate initial predictions on raw text. Annotators review and validate these suggestions, correcting any inaccuracies. Multi-Modality Support Encord platform supports annotation of different types of data including images, videos, and multi-modal datasets. This is particularly useful for cross-domain projects where text is tied to visual data. For example, in medical applications annotating entities like SYMPTOM and DIAGNOSIS in patient text reports alongside CT scans or X-rays. Similarly in multimedia data, extracting named entities from speech transcriptions in videos and linking them to visual metadata can be easily done in Encord. Export and Integration Encord makes it easy to export annotated data in formats compatible with popular NLP frameworks and tools such as spaCy, Hugging Face Transformers, TensorFlow and many more. The supported formats are JSON, CSV, JSONL (ideal for training spaCy models) etc. It helps in integrating this data into model training pipelines easily making it easier to train the model. Challenges in NER NER identifies entities such as names, organizations, locations, and more within unstructured text accurately, but it may also face challenges. Following are some of the challenges in NER. Ambiguity Ambiguity arises when a word or phrase can have multiple meanings depending on its context. NER models can struggle to correctly classify such entities, especially in the absence of sufficient context. There are two main types of ambiguity: Lexical Ambiguity: Words that can belong to multiple categories (e.g., person, organization, or location). Contextual Ambiguity: Entities that require surrounding text to determine their exact type. Example: Sentence: "I visited Jordan last summer to attend the Jordan Shoes event." Jordan (First occurrence): Refers to a location (country). Jordan Shoes: Refers to an organization (brand name). Context-sensitive words require language models capable of understanding relationships in the text. Traditional rule-based models struggle with ambiguous entities due to limited contextual awareness. Nested Entities Nested entities occur when one entity is embedded within another, creating hierarchical structures. This challenge is common in domains like legal, biomedical, or financial text. Example:  Sentence: "The University of California, Berkeley is a top-ranked university." University of California: Organization (outer entity). Berkeley: Location (nested entity within the organization name). Traditional NER models often assume that entities do not overlap, leading to errors when an entity is nested. Nested structures require advanced models that can handle multiple layers of entities (e.g., transformer-based approaches or dependency parsers). Entity Boundary Detection Entity boundary detection involves identifying the exact start and end positions of an entity. Errors can occur when entities contain compound phrases or when boundaries are unclear. Example: Sentence: "New York City Mayor Eric Adams introduced a new policy." Correct Entity: "Eric Adams" ->( PERSON) Incorrect Boundary: "New York City Mayor Eric"  -> (Partial extraction) Compound entities or multi-word entities can confuse models. Entity boundaries may vary depending on language structure and dataset consistency. Domain-Specific Entities NER models trained on general-purpose corpora (like CoNLL-2003) often fail to identify entities in domain-specific text, such as medical, legal, or financial documents. Example: Sentence: "The patient was prescribed metformin for controlling Type 2 diabetes." Entities: "metformin" -> (MEDICATION), "Type 2 diabetes" -> (DIAGNOSIS) General-purpose models may not recognize "metformin" or "Type 2 diabetes" as entities. Entities in specialized domains require custom tagging schemas and training data. Annotating large domain-specific datasets is time-consuming and expensive. Language and Morphological Variations NER models may face challenges with languages that have complex grammatical structures, lack capitalization cues, or feature multiple inflected forms of words. Example: Capitalization Issues (Lowercase or noisy text): Sentence: "steve jobs was the co-founder of apple inc." Challenge: Models relying on capitalization may miss "steve jobs" as a PERSON. Some languages (e.g., German, Finnish) have inflected words, where entity names can change forms depending on usage. Standard NER models trained on English datasets may struggle with non-English text without additional training. Key Takeaways NER identifies and classifies entities like Person, Organization, Location, and Date in text. The NER process involves text preprocessing, feature extraction, and contextual analysis using models. NER uses tagging schemes like BIO (Begin-Inside-Outside) to mark entity boundaries. NER tools help annotate training data for models. Popular tools include Encord, Prodigy, and Doccano. NER is used in information extraction, chatbots, customer feedback analysis, and healthcare and in many other applications. Tools like Encord simplify annotation, making it easier to build accurate NER models. 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.

Google DeepMind recently released three new generative AI models: Gemini 2.0. Veo 2, and Imagen 3. Each of these tools address specific areas of artificial intelligence application. Here is an explainer of what they do and how they do it: Gemini 2.0 Gemini 2.0 is the latest iteration of Google’s multimodal AI model. Building on the foundations laid by its predecessor Gemini 1.5, the llm (large language model) introduces new features that allow the developers to create more interactive agentic applications.  Example of Gemini 2.0 output Gemini 2.0 Key Features Better Performance Gemini 2.0 Flash is optimized for better performance and efficiency. It’s not only faster than Gemini 1.5 Pro, about twice the speed, but also more reliable across a range of tasks. Multimodal Capabilities Gemini can handle and generate outputs in multiple formats like text, audio, and images. Instead of just processing or generating one type of content, you can now create responses that combine all these elements through a single API call. Native Tool Integration Another key feature of Gemini 2.0 is its ability to use external tools. Unlike earlier models, Gemini 2.0 can natively call tools like Google Search, execute code, and interact with third-party functions. This means you can now use these tools directly in your applications. For example, the Gemini model can search for information in real-time, pulling from multiple datasets simultaneously to deliver more accurate and comprehensive answers. Multimodal Live API This API supports real-time inputs, including audio and video streaming, enabling the creation of dynamic, interactive applications. It helps create features like voice activity detection, real-time video processing, and conversational interruptions, which are particularly useful in applications like virtual assistants, interactive learning platforms, and media streaming.  {For more information, read the blog by Google: The next chapter of the Gemini era for developers} Gemini 2.0 Applications Google Gemini 2.0 is a significant step toward the creation of more autonomous AI systems, known as agentic models. These are AI systems that not only process and generate information but can also take actions on behalf of the user, with supervision. Here are some of the ai agents by Google: Project Astra: A general-purpose assistant for everyday tasks, which interprets information from multiple sources to assist users. Project Mariner: An ai agent designed for autonomous web navigation, enabling tasks like information retrieval or form completion. It simplifies online interactions by automating routine actions, saving users time and effort. Jules: A coding assistant that suggests code snippets, generates scripts, and understands programming contexts to speed up development workflows. Gemini 2.0 isn’t just about automating tasks—it’s focused on dynamic interaction with its environment, adapting to user needs to provide more efficient and tailored solutions. Availability and Accessibility Gemini 2.0 is available for developers via Google AI Studio and Vertex AI, with wider availability expected in early 2025. Veo 2 Veo 2 creates 8-second ai video clips at 4K resolution (720p at launch) with a significant improvement in cinematic control and realism. The new model incorporates better physics simulation and reduced hallucinations, allowing more accurate movement and detail in the generated videos. It has outperformed competitors, including OpenAI’s Sora, in head-to-head human evaluations, scoring higher in prompt adherence and output quality, providing state-of-the-art results. Veo 2 Key Features Realistic Detail and Human Movements Since Veo 2 has a better understanding of real-world physics, human expressions, and movements, it generates more accurate and lifelike ai videos. This makes it ideal for both creative and professional usecase. Cinematographic Precision In Veo 2, you can specify the type of shot you want, whether it is a low angle tracking shot, a close-up of a person, etc. For example, asking for a shot with an “18mm lens” or “shallow depth of field” will deliver an output that matches the unique properties of those cinematic tools. Longer Videos Veo 2 supports video generation at resolutions up to 4K and extended video lengths, making it suitable for a variety of projects, from short-form content to more detailed, longer productions. Reduced Hallucinations While some video generation models tend to “hallucinate” unwanted details like extra fingers or objects, Veo 2 has improved its ability to generate more accurate, realistic visuals, making these issues less frequent and providing higher quality outputs. Veo 2 Applications Content Creation: Helps creators generate high-quality videos for editing or concept development. Entertainment: Supports industries like film and gaming with realistic animations and dynamic visuals. Availability and Accessibility Veo 2 can be accessed through Google Labs and VideoFX for users interested in video generation, with future integration into YouTube Shorts and Vertex AI. All videos generated with Veo 2 come with an invisible SynthID watermark, which helps identify AI-generated content and ensures ethical use by reducing the risk of misinformation and misattribution. Imagen 3 Imagen 3 is the latest version of Google’s cutting-edge image-generation model. It focuses on creating high-quality, detailed images from textual descriptions. The model’s updates improve the quality and versatility of its outputs. Image generated by Imagen 3 (Source) Imagen 3 Key Features Better Composition and Lighting: Outputs are more refined, with better attention to visual accuracy. Diverse Art Styles: Supports generating images in multiple styles, from photorealistic to abstract. From photorealism to impressionism, abstract art to anime, Imagen 3 can now produce these styles with greater accuracy and more detail than before Artifact Reduction: Fewer visual imperfections compared to previous versions. More Accurate Prompt Following: The model now better understands and follows text prompts, allowing for more precise outputs.  Imagen 3 Applications Art and Design: Assists in rapid prototyping of visual concepts. Marketing: Generates custom visuals for use in advertisements or product promotions. Availability and Accessibility Imagen 3 is now available globally through ImageFX, accessible in over 100 countries for users who want to create high-quality images from text prompts. How These Tools Work Together While each of these ai-powered model serves different purposes, they complement each other. For example, Gemini 2.0’s agent capabilities could use Imagen 3 to generate custom visuals, Veo 2 to produce videos, or Whisk to create personalized content by remixing inputs such as images of subjects, scenes, and styles. This interoperability creates opportunities for better AI ecosystems. Key Highlights Gemini 2.0: Enhanced performance, multimodal capabilities, and real-time API for dynamic, interactive applications. Veo 2: High-quality, cinematic video generation with improved realism and extended video lengths. Imagen 3: Advanced image generation with better composition, diverse art styles, and improved accuracy in prompt following.

In April 2023, we introduced the original SAM model to our platform a mere few days after its initial release. Today, we are excited to announce the integration of Meta’s new Segment Anything Model, SAM 2, into our automated labelling suite, just one day after its official release. This rapid integration underscores our commitment to providing our customers with access to cutting-edge machine learning techniques faster than ever before. Integrating SAM 2 brings enhanced accuracy and speed to your automated segmentation workflows, enhancing both throughput and user experience. We’re starting today by bringing SAM 2 into image segmentation tasks, where it’s been benchmarked to perform up to 6x faster than SAM. We are also looking forward to introducing the VOS capabilities of SAM 2, enhancing performance on automating video segmentation technologies already in Encord, such as SAM + Cutie. As an extremely new piece of technology, SAM 2 is being made available to all our customers via Encord Labs. To enable SAM 2, navigate to Encord Labs in your settings and enable the switch for SAM 2, as illustrated in our documentation. When you return to the editor, you’ll know SAM 2 is enabled by the enhanced magic wand icon in the editor, signalling that you are using the latest and most powerful tools for your annotation tasks. How Encord's SAM 2 Integration Increased Annotation Efficiency & Cost Savings for Plainsight Plainsight faced significant challenges with their in-house data pipelines, which were resource-intensive and inefficient. Their homegrown solutions struggled to meet their high-standards, diverting focus from their core mission. Encord’s Automated Labeling Suite, including tools like SAM 2 assisted labeling, boosted annotator productivity, reduced manual annotation time and costs by approximately 50%. Kit (CEO, Plainsight) says, “Before using Encord, it was challenging to see all the data, projects, and annotations in one place. I constantly had to ask questions to understand what was going on. Now, with Encord I feel like we have a much clearer understanding of everything that's happening.” Plainsight transitioned to Encord’s data development platform, which seamlessly integrated with their existing pipelines. Encord provided robust data management, automated annotation tools, and granular curation features, enabling Plainsight to eliminate their inefficient in-house solutions and focus on core objectives. The Plainsight team specifically mentioned the automated annotation tooling, notably the SAM 2 model, as a key improvement over their previous set-up. Read the full Plainsight Case Study to see how you can also slash data management overhead. Try Out Encord's SAM 2 Integration We are eager for our customers to try out SAM 2 and experience its benefits firsthand. We believe that this integration will significantly enhance the capabilities of our platform and provide unparalleled accuracy and speed in data annotation. We invite all users to send their feedback to product@encord.com. Your insights are invaluable as we continue to push the boundaries of what’s possible in machine learning annotation and evaluation. Thank you for being a part of this exciting journey with Encord. We look forward to continuing to deliver world-leading technology at a rapid pace to meet the needs of our innovative customers. To implement the SAM 2 model, read our comprehensive guide on How To Fine-Tune Segment Anything, which also includes a Colab notebook as a walkthrough.

The global machine learning industry is projected to reach $79 billion by the end of 2024, with a remarkable 38% year-over-year growth. Within this expanding landscape, computer vision and image recognition remain critical components, expected to reach $25.8 billion this year with sustained growth projections through 2030. However, the foundation of these advanced AI systems—image annotation—faces persistent challenges that significantly impact model performance. Poor-quality images and inconsistent labeling processes create substantial bottlenecks in AI development pipelines. Blurry or low-resolution images hinder accurate object recognition, while manual annotation processes prove time-consuming and costly, especially when dealing with large datasets.  The complexity increases further when dealing with overlapping objects, challenging backgrounds, and varying lighting conditions, all of which demand sophisticated annotation approaches. Selecting the appropriate image annotation tool becomes crucial as it directly influences the quality of training data and, subsequently, model performance. The right choice can result in accurate annotations and poor performance in object detection, recognition, and classification tasks.  A strategic approach to tool selection must consider not only the immediate annotation requirements but also scalability, quality control mechanisms, and the specific needs of the annotation workflow. The stakes are high, as image labelling is the key to developing reliable and accurate AI models. Understanding Image Labeling Tools Image labeling tools are essential for assigning textual or numerical annotations to objects within images and videos. These tools are the foundation for training computer vision models across diverse industries, from autonomous vehicles to healthcare diagnostics. In practical applications, these tools enable businesses to perform critical tasks such as object detection, tracking, and localization. For instance, retail companies use bounding box labeling to track in-store products and monitor inventory movements, while healthcare providers employ polygon annotation techniques for organ identification in medical imaging. While manual annotation remains common, modern labeling platforms incorporate quality control mechanisms and validation processes to ensure consistent and accurate annotations across large datasets. The quality of these annotations significantly influences computer vision systems' performance, making tool selection crucial for organizations developing AI applications.  Essential Features of Modern Image Labeling Tools The evolution of image labeling tools has led to sophisticated platforms combining precision and efficiency. Modern solutions now offer integrated features that streamline annotation while maintaining high accuracy standards. Annotation Capabilities The foundation of any robust image labeling tool lies in its annotation versatility. At the core, these platforms support multiple annotation types to accommodate various computer vision tasks: Bounding Boxes and Polygons Bounding boxes are the primary annotation method for object detection, using coordinate pairs to define object locations. For objects with irregular shapes, polygonal segmentation enables a more precise boundary definition, which is crucial for applications like medical imaging and autonomous vehicle perception. Semantic Segmentation Advanced tools now support pixel-wise annotation capabilities, where each pixel receives a class assignment. This granular approach proves essential for applications requiring detailed scene understanding, such as urban environment analysis for autonomous vehicles. These capabilities significantly enhance annotation productivity without switching between tools to multitask annotations. Technical Requirements Modern image labeling platforms must handle diverse data complexities while maintaining performance at scale. These tools now support high-resolution images with 16-bit or higher color depth, enabling precise annotation for specialized industries like medical imaging and satellite photography. The technical infrastructure of these platforms accommodates complex image formats and specialized data types, including medical imaging formats like NRRD and NiFTI. This versatility is essential for healthcare applications and research institutions with domain-specific image types. Scalability remains critical, with leading platforms like Encord supporting datasets of up to 500,000 images.  Storage and Processing Advanced platforms integrate with various storage solutions, including AWS S3, Google Cloud Platform, and Azure, enabling efficient data management for large-scale projects. Web-based interfaces reduce local resource requirements while maintaining robust performance through optimized visualization settings and flexible layouts.  AI Integration Modern image labeling platforms leverage advanced AI capabilities to enhance annotation efficiency and accuracy. These systems combine human expertise with machine learning to create a more streamlined workflow. Transfer Learning and Pre-trained Models The integration of transfer learning enables platforms to leverage pre-trained models for initial feature extraction, allowing annotators to focus on refinement rather than starting from scratch.  This approach proves helpful when working with limited labeled datasets, as it helps maintain consistency while reducing the manual workload. Quality Control Mechanisms Advanced platforms implement robust quality control through: Gold set evaluation for measuring annotator performance and consistency Continuous monitoring of annotation quality through automated checks Majority voting systems to reduce individual bias and errors Active Learning Integration The platforms employ active learning algorithms that strategically select images for annotation, optimizing the labeling process. This system identifies: High-priority images that require immediate attention Complex cases that need expert review Performance patterns to assess annotator reliability Automated Validation Quality control mechanisms automatically validate labeled data against established benchmarks, enabling: Cross-validation of annotations Inter-rater reliability checks Systematic error detection and correction These AI-powered features significantly reduce annotation time while maintaining high accuracy standards, making them essential for large-scale data labeling projects. Get a demo of Encord and see how AI-assisted labeling can reduce your annotation time by 70% Quality Control and Workflow Management Quality control in image labeling directly impacts model performance, with studies showing that 10-30% of errors in datasets stem from human labeling mistakes. Implementing robust quality management systems can significantly reduce these errors while improving team productivity. Team Collaboration Multi-user Support Modern platforms enable concurrent annotation work through role-based access control systems. Teams can work simultaneously on datasets while maintaining consistent labeling standards through unified interfaces. This collaborative approach enables: Reviewer permission levels for label validation Comment systems for feedback loops Issue tracking for quality improvements Review and Validation Implementing structured review cycles reduces labeling errors and prevents downstream modeling issues. Quality metrics track consensus between labelers and measure performance against ground truth datasets. Teams using automated quality control systems have reduced labeling costs by up to 30% within three months. Data Management Dataset Organization Effective data management systems support: Cloud storage integration for scalable operations Automated data import/export capabilities Customizable export formats for different ML frameworks7 Version Control Modern platforms implement comprehensive version tracking that enables: Historical review of annotation changes Dataset iteration management Quality metric tracking across versions Integrating these features creates a streamlined workflow that significantly reduces annotation time while maintaining high accuracy standards. Organizations implementing these systems report up to 50% time savings in annotation tasks while maintaining quality standards. This efficiency gain becomes valuable when dealing with large-scale datasets requiring multiple refinement iterations. Security and Compliance Enterprise-grade security features are essential for companies looking to scale image labeling operations across their organization while meeting stringent regulatory requirements across industries. Data Protection Infrastructure Image labeling platforms implement comprehensive security measures through: Enterprise-grade encryption for data at rest and in transit Secure cloud storage integrations with AWS S3 and similar platforms Data anonymization protocols for sensitive information Access Control Systems Modern platforms enforce strict access management through: Role-based permissions for different user levels Multi-factor authentication Audit trails for all system activities Secure access integrations with version control systems   Table 1: Enterprise Security and Compliance Requirements for Image Labeling Platforms - A Comprehensive Assessment Matrix These security and compliance measures are crucial for organizations handling sensitive data in the healthcare, life sciences, and government sectors. Get enterprise-ready with SOC2 and HIPAA-compliant labeling at with Encord. {{light_callout_end} How Encord Meets These Requirements Encord's platform delivers comprehensive solutions for modern image labeling challenges through advanced automation and scalable infrastructure. Encord is a data development platform for managing, curating and annotating large-scale multimodal AI data such as image, video, audio, document, text and DICOM files. Transform petabytes of unstructured data into high quality data for training, fine-tuning, and aligning AI models, fast.  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 and 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 surface and correct errors in labels and data. Core Strengths AI-Assisted Labeling The platform achieves remarkable efficiency through automated labeling capabilities: Automates up to 97% of annotations while maintaining 99% accuracy Reduces annotation time by up to 70% across large datasets Leverages state-of-the-art Meta AI's Segment Anything Model 2 (SAM 2) for pixel-perfect segmentation Figure: Auto annotations using SAM model on DICOM images in Encord Platform (Source) Scalable Infrastructure The system effectively handles datasets ranging from 1,000 to 10,000,000 images, supporting: Native support for specialized formats like DICOM alongside image and video files Programmatic data upload using the Encord SDK Enterprise-grade security with SOC2, HIPAA, and GDPR compliance Advanced Quality Control The platform implements comprehensive quality management: Dynamic sampling rates for review processes Annotator-specific routing and weighted distribution Full audit trails for regulatory compliance Real-world implementation demonstrates significant improvements, with organizations achieving the following: 60% increase in labeling speed 20% increase in mean Average Precision (mAP) Reduction from one year to two months in model development time for large scale imaging projects Figure: Encord - Key Features Selection Criteria for Image Labeling Tools When evaluating image labeling tools, organizations must consider multiple factors impacting project success and return on investment. Evaluation Metrics Ease of Use The platform's interface should minimize cognitive load while maximizing efficiency through: Intuitive drawing tools and vector pen capabilities Streamlined user interface for various devices Quick loading time, even with numerous objects per image Scalability Tools must demonstrate robust scaling capabilities across: Dataset size handling User management and collaboration Integration with existing ML frameworks Cost-effectiveness ROI considerations should include: Reduction in labeling time through automation Decreased costs through model-assisted labeling (up to 50% savings) Resource optimization through quality control features Considerations Project Alignment Evaluate tools based on: Specific use case requirements Data types and annotation methods needed Security certifications and compliance needs Technical Infrastructure The assessment should cover the following: API and SDK availability Integration capabilities with ML frameworks Storage options and data handling capacity The selection process should prioritize tools that offer comprehensive features while maintaining flexibility for project-specific requirements. Organizations should evaluate immediate needs and long-term scalability potential when making decisions. Conclusion The evolution of computer vision applications demands sophisticated image-labeling tools that balance automation, accuracy, and scalability. As organizations scale their AI initiatives, selecting the right annotation platform becomes crucial for maintaining data quality while optimizing resources. Modern image labeling solutions must incorporate AI-assisted automation, robust quality control mechanisms, and enterprise-grade security features. Integrating micro-models, active learning algorithms, and comprehensive workflow management tools can significantly reduce annotation time while maintaining high accuracy standards. Looking ahead, image labeling tools will continue to evolve with enhanced AI capabilities, improved automation, and more sophisticated quality control mechanisms. Integrating foundation models and specialized micro-models will streamline annotation while maintaining human oversight for critical decisions. Encord's platform addresses these requirements through its comprehensive feature set, delivering significant efficiency gains across various industries. Organizations seeking to optimize their computer vision workflows can explore Encord's solutions, which have successfully reduced model development time from years to months while maintaining high accuracy in automated annotations. Key Takeaways: Image Labeling Tools Image labeling is critical for AI development but faces challenges like poor image quality and inconsistent annotations, impacting model performance. Modern tools require versatile annotation capabilities (e.g., bounding boxes, polygons, semantic segmentation), technical robustness, and scalability to handle complex, large-scale datasets. Integrating AI features like transfer learning, active learning, and automated validation enhances efficiency and accuracy in labeling workflows. Quality control mechanisms, workflow management, and stringent security and compliance features are essential for maintaining data integrity and meeting regulatory standards. Encord's platform meets these requirements with AI-assisted labeling, advanced quality control, and enterprise-grade security, significantly improving efficiency and reducing model development time. ⚙️ Create high-quality training data up to 10x faster with the most advanced image labeling tool with Encord's Image Annotation Tool.