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Text classification is a process to teach machines to automatically categorize pieces of text into predefined categories or classes. Think of it like having a smart assistant that can sort your emails into "work," "personal," and "spam" folders, or a system that can determine whether a movie review is positive or negative.  E-Mail Sorting using Text Classification Now, let's explore how machines actually "read" and understand text, which is quite different from how humans do it. Unlike humans, machines can not naturally understand words and their meanings. Machines work with numbers, not text. Therefore human language is transformed into a format that machines can process mathematically. This is done by converting words into numbers. Imagine you're teaching a computer to understand text the way you might teach a child to understand a new language using building blocks. The first step is to break down text into smaller pieces. Words are converted to numbers through various methods. One simple approach is "one-hot encoding," where each word gets its own unique number or vector. More advanced methods like "word embeddings" represent words as points in a multi-dimensional space, where similar words are closer together. For example, in a basic number system, the sentence "I love pizza" might become something like [4, 12, 8], where each number represents a word. Once text is converted to numbers, machines can start recognizing patterns. It learns that certain number combinations (representing words) often appear together in specific categories. For example, in restaurant reviews, positive reviews might often contain number patterns representing words like "delicious," "excellent," "amazing" and negative reviews might show patterns representing "disappointing," "cold," "poor". Machines also learn the order of words and the meaning of combinations. For better understanding, they break down the following: Word order: "The dog chased the cat" is different from "The cat chased the dog" Context: The word "bank" means something different in "river bank" versus "bank account" Relationships: Understanding that "excellent" and "outstanding" are similar in meaning Finally, the machine uses this processed information to make classification decisions. It's similar to how you might recognize a song's genre by picking up on certain patterns of instruments, rhythm, and style. For example, If the a machine sees a new sentence like: "The weather today is sunny and warm."  It might classify it as Sunny Weather because it recognizes patterns from previous examples. While machines process text very differently from humans, the goal is to approximate human-like understanding. Here’s how this process relates to how humans read: How Humans Read and Classify Text How Machines Read and Classify Text The main difference is that humans naturally understand meaning, while machines rely on patterns and probabilities. The Evolution of Text Classification Techniques Over the years, various methods have been developed for text classification. These methods or techniques range from traditional machine learning algorithms to advanced deep learning techniques. Let’s look at some of these methods: Rule-Based Methods Rule-based methods are one of the oldest and most intuitive approaches to text classification. These systems rely on manually crafted linguistic rules that are specifically designed to identify patterns or characteristics within the text and assign predefined categories. Despite being traditional, they remain relevant in certain contexts where domain-specific knowledge and interpretability are critical. Rule-based methods classify text by applying logical conditions, often written as if-then rules. These rules use features such as: Keywords or Phrases: Specific words or combinations of words that indicate a category. Example: Emails containing words like "win", "lottery", or "prize" might be classified as spam. Regular Expressions: Patterns to detect variations of text. Example: Identifying email addresses or phone numbers. Linguistic Features: Syntax, parts of speech, or other linguistic markers. Example: A sentence starting with “Dear” could indicate a formal letter. Traditional Machine Learning Algorithms Traditional machine learning algorithms are a cornerstone of text classification. Unlike rule-based methods, these algorithms learn patterns from labeled data, making them more scalable and adaptable to diverse tasks. Below is an explanation of some of the most widely used traditional algorithms for text classification. Naive Bayes Classifier Naive Bayes is a probabilistic classifier based on Bayes’ Theorem. It assumes that features (words, in text classification) are independent of each other—a "naive" assumption, hence the name. Despite this assumption, it performs well in many real-world scenarios. Calculates the probability of a text belonging to a class using: The class with the highest probability is chosen as the predicted category. Support Vector Machines (SVM) SVM is a powerful supervised learning algorithm that finds the best boundary (hyperplane) to separate classes in a high-dimensional space. It works well with sparse datasets like text. SVM maximizes the margin between data points of different classes and the decision boundary. SVM can handle non-linear relationships using kernels (e.g., polynomial or radial basis function (RBF) kernels). Support Vector Machines  The above figure shows how SVM separates two classes (Positive and Negative) by finding the optimal hyperplane (black line) that maximizes the margin (blue region) between the closest data points of both classes, called support vectors. Decision Trees Decision trees classify data by splitting it based on feature values in a hierarchical manner. The structure resembles a tree where each internal node represents a feature, branches represent decisions, and leaf nodes represent categories. Splits data recursively based on features that maximize information gain or reduce entropy (using criteria like Gini Index or Information Gain). Classification follows the path from the root node to a leaf node. Text Representation of the Decision Tree for Positive and Negative classes In the above figure, the decision tree predicts sentiment (Positive or Negative) based on the presence of specific words in the text. It evaluates whether words like "one" and "not" appear in the text and uses these conditions to classify the sentiment. K-Nearest Neighbors (KNN) KNN is a simple, non-parametric algorithm that classifies data points based on the majority class among their k nearest neighbors in the feature space. It calculates the distance (e.g., Euclidean, cosine) between the new data point and all other points in the dataset. The class of the k closest points is assigned to the new data point. K-Nearest Neighbors The above figure illustrates the KNN algorithm. It shows how a new example (yellow square) is classified based on the majority class (Positive or Negative) of its nearest neighbors (k=3 or k=7) in the feature space. Deep Learning Techniques Deep learning has revolutionized text classification by introducing methods capable of learning complex patterns and capturing contextual relationships. These techniques have significantly outperformed traditional methods in many NLP tasks. Let’s explore the key players in deep learning-based text classification. Convolutional Neural Networks (CNNs) While CNN are widely known for their success in image processing, they are also highly effective for text classification tasks. In text classification, CNNs capture local patterns like n-grams (e.g., phrases or sequences of words) and use these patterns to classify text into predefined categories. Before a CNN can process text, the text must be converted into a numeric format. It first converts text into a numeric format (e.g., word embeddings like Word2Vec or GloVe). Applies convolutional filters over the embeddings to capture local patterns. Uses pooling layers (e.g., max-pooling) to reduce dimensions and focus on the most important features. Final dense layers classify the text into predefined categories. A CNN Architecture for Text Classification (Source) Recurrent Neural Networks (RNNs) RNNs are a type of neural network designed specifically for processing sequential data, making them well-suited for text classification tasks where the order and relationships between words are important. RNNs excel in tasks like sentiment analysis, spam detection, and intent recognition because they can model contextual dependencies within a sequence.  RNNs handle input data as sequences, processing one element at a time. This sequential approach allows them to capture temporal dependencies and patterns within the data. At each time step, the RNN maintains a hidden state that serves as a memory of previous inputs. This hidden state is updated based on the current input and the previous hidden state, enabling the network to retain information over time. Unlike traditional neural networks, RNNs share the same weights across all time steps. This weight sharing ensures that the model applies the same transformation to each element in the sequence, maintaining consistency in how inputs are processed. At each time step, the RNN produces an output based on the current hidden state. Depending on the task, this output can be used immediately (e.g., in sequence-to-sequence models) or accumulated over time (e.g., in sentiment analysis) to make a final prediction. Training RNNs involves adjusting their weights to minimize errors in predictions. This is achieved using a process called Backpropagation Through Time, where the network's errors are propagated backward through the sequence to update the weights appropriately. Standard RNNs can struggle with learning long-term dependencies due to issues like the vanishing gradient problem. To address this, architectures such as Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs) have been developed. These variants include mechanisms to better capture and retain long-term information. RNN Model for Text Classification (Source) LSTM Long Short-Term Memory (LSTM) networks are a type of recurrent neural network (RNN) designed to capture long-range dependencies in sequential data. This makes LSTM effective for text classification tasks. Traditional RNNs can struggle with learning long-term dependencies due to issues like the vanishing gradient problem. LSTMs address this by incorporating memory cells and gating mechanisms that regulate the flow of information. This enables the network to retain or forget information as needed. This architecture allows LSTMs to maintain context over longer sequences which is important for understanding the meaning of text where context can span multiple words or sentences. A workflow for using LSTMs in text classification involves several key steps: Text Preprocessing In text processing it first performs tokenization which splits text into individual words or tokens. Then perform stopword removal to eliminate common words that may not contribute significant meaning (e.g., "and," "the"). After this stemming/lemmatization is performed to reduce words to their base or root form (e.g., "running" to "run").  Text Representation It converts words into dense vector representations that capture semantic meaning. Pre-trained embeddings like GloVe or Word2Vec are often used to provide meaningful word vectors.  Training After this training is performed. The LSTM model architecture for text classification consists of following layers: Embedding Layer: Transforms input tokens into their corresponding word embeddings. LSTM Layer: Processes the sequence of embeddings to capture dependencies and context. Dense Layers: Fully connected layers that interpret the LSTM's output and perform the final classification. The architecture commonly uses binary cross-entropy loss function for binary classification and categorical cross-entropy  loss function for multi-class classification. It uses optimizers  like Adam for optimizing the model's weights. LSTM sequence model (Source) LSTM networks are a powerful tool for text classification tasks, capable of capturing the sequential nature and contextual dependencies inherent in language. Transformers A transformer is a deep learning model architecture introduced in the paper "Attention is All You Need" by Vaswani et al. (2017). It is designed to handle sequential data, such as text, by using a mechanism called self-attention to understand the relationships between words in a sentence or a document regardless of their position. Transformers are foundational to many state-of-the-art NLP models like BERT, GPT, and T5. In traditional sequence models, such as RNNs LSTMs, words are processed sequentially, one at a time. This sequential nature makes it difficult for these models to capture long-range dependencies efficiently, as the information about earlier words may fade as processing continues. Transformers, however, process all words in a sequence simultaneously, allowing them to capture both short-range and long-range dependencies effectively. While the original transformer architecture (introduced in "Attention is All You Need") did use an encoder-decoder structure, many modern transformers used for text classification (like BERT) are actually encoder-only models. They don't have a decoder component. This is because text classification doesn't require the generative capabilities that the decoder provides. The encoder comprises multiple layers of self-attention mechanisms and feedforward neural networks. Each word in the input sequence is first converted into a dense numerical representation called an embedding. These embeddings are then processed by the self-attention mechanism, which computes the importance of each word relative to others in the context of the sequence. This allows the model to focus on the most relevant words for a given task while still considering the entire sequence. For text classification, the typical workflow with transformers involves the following steps: First, the text goes through tokenization (e.g. WordPiece or Byte-Pair Encoding etc.). Imagine breaking down a sentence "The cat sat" into pieces like ["The", "cat", "sat"]. The transformer actually breaks it into even smaller subword units, so "walking" might become ["walk", "ing"]. This helps it handle words it hasn't seen before. These tokens are then converted into numerical vectors called embeddings. Each token gets transformed into a long list of numbers that capture its meaning. The word "cat" might become something like [0.2, -0.5, 0.8, ...]. These numbers encode semantic relationships - similar words will have similar number patterns. Next comes the heart of the transformer, the self-attention mechanism. This is where the model looks at relationships between all words in your text simultaneously. When processing the word "it" in a sentence, the model might pay strong attention to a noun mentioned earlier to understand what "it" refers to. The model calculates attention scores between every pair of words, creating a web of relationships. The transformer has multiple layers (called transformer blocks) that each perform this attention process. In each layer, the word representations get refined based on their contexts. Early layers might capture basic grammar, while deeper layers understand more complex relationships and meaning. For classification transformers use a special [CLS] token added at the start of the text. This token acts like a summary through all those attention layers. Think of it as the model's way of taking notes about the overall meaning. After all the transformer layers, the final [CLS] token representation goes through a classification head - typically a simple neural network that maps this rich representation to your target classes. If you're doing sentiment analysis, it might map to "positive" or "negative". For topic classification, it could map to categories like "sports", "politics", etc. The output layer applies a softmax function to convert these final numbers into probabilities across your possible classes. The highest probability indicates the model's prediction. For instance, in a sentiment analysis task, the transformer learns to focus on words or phrases like "excellent," "terrible," or "average" in their respective contexts. By training on a labeled dataset, the model adjusts its parameters to associate specific patterns in the embeddings of the input text with corresponding class labels (e.g., positive, negative, or neutral sentiment). BERT for text classification (Source) Teaching Machines to Read and Classify Text Text classification is a task in NLP where machines are trained to assign predefined categories to pieces of text. It plays a critical role in tasks like sentiment analysis, spam detection, topic categorization, and intent detection in conversational AI. Key Components of Text Classification Systems Text Input: The system processes raw text such as sentences, paragraphs, or entire documents. Preprocessing: Text is cleaned, tokenized, and converted into numerical representations (embeddings) that models can understand. Modeling: A machine learning model, often based on transformers like BERT or DistilBERT, learns patterns and relationships in the text to classify it into one or more categories. Output: The system outputs a category label or probability distribution over multiple categories. Here’s a simple example of how to train a text classification model using Transformers in a Google Colab notebook. We’ll use the Hugging Face transformers library, which provides a user-friendly interface for working with transformer models like BERT. Following are the steps: Import the required libraries. Load a pre-trained transformer model. Use a small dataset (e.g., the IMDb dataset for sentiment analysis). Fine-tune the model for text classification. Now we will see step-by-step example: First install the required libraries from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments from datasets import load_dataset import torch Step 1: Load the Dataset In this step, we load the IMDb movie review dataset, which contains movie reviews labeled as either positive or negative. We then split the dataset into two parts: one for training the model and one for testing its performance. A smaller subset of 2000 training samples and 500 test samples is used for faster processing. # Step 1: Load the dataset dataset = load_dataset("imdb") # Split into train and test train_dataset = dataset['train'].shuffle(seed=42).select(range(2000)) # Use a subset for quick training test_dataset = dataset['test'].shuffle(seed=42).select(range(500)) Step 2: Load the Tokenizer and Model We load a pre-trained BERT model and its associated tokenizer. The tokenizer converts text into numerical format (tokens) that the model can understand. The BERT model is set up for a sequence classification task with two possible outputs: positive or negative sentiment. # Step 2: Load the tokenizer and model model_name = "bert-base-uncased" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2) Step 3: Preprocess the Dataset Here, we prepare the dataset for the model by tokenizing the text reviews. Tokenization ensures all reviews are represented as sequences of numbers, with longer reviews truncated to a maximum length of 128 tokens and shorter ones padded to maintain consistency.  The original text column is removed from the dataset since the model only needs the tokenized data. The dataset is also converted into a format that the PyTorch framework can process. # Step 3: Preprocess the dataset def preprocess_function(examples): return tokenizer(examples["text"], truncation=True, padding=True, max_length=128) train_dataset = train_dataset.map(preprocess_function, batched=True) test_dataset = test_dataset.map(preprocess_function, batched=True) # Remove unnecessary columns train_dataset = train_dataset.remove_columns(["text"]) test_dataset = test_dataset.remove_columns(["text"]) train_dataset.set_format("torch") test_dataset.set_format("torch") Step 4: Define Training Arguments We define the settings for training the model. This includes the number of epochs (3), batch size (16), learning rate, logging frequency, and saving the best model after training. These arguments control how the model learns and evaluates its performance during training. # Step 4: Define training arguments training_args = TrainingArguments( output_dir="./results", evaluation_strategy="epoch", learning_rate=2e-5, per_device_train_batch_size=16, per_device_eval_batch_size=16, num_train_epochs=3, weight_decay=0.01, logging_dir='./logs', logging_steps=10, save_strategy="epoch", load_best_model_at_end=True, ) Step 5: Initialize the Trainer We set up the Hugging Face Trainer, which simplifies the training and evaluation process. The Trainer combines the model, training settings, and datasets, making it easier to manage the training pipeline. # Step 5: Initialize the Trainer trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, eval_dataset=test_dataset, ) Step 6: Train the Model In this step, the model learns to classify the sentiment of reviews (positive or negative) by training on the prepared dataset. It iteratively adjusts its internal parameters to minimize the error in its predictions. # Step 6: Train the model trainer.train() Training Results on Weights & Biases (W&B) Step 7: Evaluate the Model Finally, the trained model is evaluated on the test dataset. This step calculates metrics like loss and provides insights into how well the model performs on unseen data. # Step 7: Evaluate the model results = trainer.evaluate() Step 8: Test the model This step evaluates how well the trained model performs on the test dataset. It involves generating predictions for the test samples, comparing these predictions to the actual labels, and calculating accuracy manually. # Step 8: Test the model # Get predictions and labels from the evaluation predictions, labels, _ = trainer.predict(test_dataset) # Convert logits to predicted class indices predicted_classes = predictions.argmax(axis=-1) # Calculate accuracy manually accuracy = (predicted_classes == labels).mean() print(f"Test Accuracy: {accuracy:.4f}") Following is the output Step 9: Test on a Sample Text This step demonstrates how to use the trained model to classify a single piece of text. It involves preparing the text, passing it through the model, and interpreting the result. # Step 9: Test on a sample text # Check if GPU is available and use it device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Move the model to the appropriate device model = model.to(device) # Test on a sample text sample_text = "This movie was amazing, I loved it!" inputs = tokenizer(sample_text, return_tensors="pt", truncation=True, padding=True, max_length=128) # Move inputs to the same device as the model inputs = {key: value.to(device) for key, value in inputs.items()} # Perform inference output = model(**inputs) # Interpret the Prediction prediction = output.logits.argmax(dim=1).item() # Display the Result print(f"Prediction: {'Positive' if prediction == 1 else 'Negative'}") Following it the output Advancements in Pre-trained Language Models BERT, GPT, and other pre-trained models have revolutionized text classification by providing contextualized understanding, transfer learning, and generalization. They outperform traditional methods in accuracy, scalability, and adaptability. As these models evolve, they continue to redefine the boundaries of NLP and text classification. Transformers have the ability to model complex language relationships and can enhance text classification tasks. By introducing innovative architectures like attention mechanisms and pre-training on massive datasets, these models bring contextual understanding and efficiency to natural language understanding and text classification. Here's how transformers like BERT and GPT improve text classification under key aspects: Contextualized Understanding Traditional approaches to text classification often relied on static word embeddings (e.g., Word2Vec, GloVe), where a word's representation remained the same regardless of its context. Transformers revolutionized this by generating dynamic embeddings, where the meaning of a word adapts based on its surrounding words. For example, the word "bank" in "river bank" versus "financial bank" is understood differently by models like BERT. This ability to model both short-range and long-range dependencies ensures better comprehension of sentence structure and meaning, which is critical for accurate classification tasks such as sentiment analysis or spam detection. Bidirectional Context Models like BERT introduced a concept of reading text in both directions (left-to-right and right-to-left). This bidirectional nature enables a richer understanding of context compared to unidirectional models because it considers the entire sentence when interpreting a word. For example, in the sentence "The movie was not great," a bidirectional model correctly interprets "not" in relation to "great" to identify a negative sentiment. This depth of understanding makes BERT particularly powerful for nuanced tasks such as intent classification or fake news detection. Attention Mechanisms Transformers use self-attention mechanisms, which allow the model to focus on the most relevant words or phrases in a sentence, regardless of its position. This is useful for classifying long texts, where critical information may appear far apart in the document. For example, in classifying legal or academic documents, a transformer can prioritize key phrases that determine the overall category, even if they are scattered throughout the text. Pre-training and Fine-tuning Transformers are pre-trained on a large database. It helps transforms to learn a broad understanding of language, and then fine-tuned on task-specific data. This two-stage process reduces the need for large labeled datasets for classification tasks. For example, a pre-trained BERT model can be fine-tuned on a smaller dataset to classify customer reviews into positive, neutral, or negative sentiments with high accuracy. This approach not only improves performance but also lowers the barrier to deploying high-quality classification models. Few-shot and Zero-shot Learning Generative transformers like GPT have brought forward the capability of few-shot and zero-shot learning. These models can generalize to new classification tasks with minimal or no additional training by using prompts. For example, GPT-4o can classify emails as "important" or "not important" with just a few examples provided as part of the input prompt. This flexibility is a major leap forward, enabling rapid deployment of classification models without extensive labeled data. Scalability and Multi-task Learning Transformers like RoBERTa and T5 extend the capabilities of BERT and GPT by improving pre-training objectives and scalability. These models can handle multiple classification tasks simultaneously, such as categorizing customer queries by department and detecting sentiment in the same input. This scalability is invaluable for businesses that need robust systems for diverse text classification needs. Transfer Learning By transfer learning, transformers have drastically reduced the time and computational resources needed to build robust text classification models. Once a model like BERT or GPT is pre-trained, it can be fine-tuned for diverse tasks like topic classification or intent detection, even with limited domain-specific data. This versatility has made text classification more accessible across industries, from healthcare to finance. Encord's Approach to Text Classification Workflows 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 offers features for text classification workflows. Encord enables efficient data management, annotation, and model training for various NLP tasks. Here's how Encord supports text classification: Document and Text Annotation Encord's platform facilitates the annotation of documents and text files, supporting tasks such as: Text Classification: Categorize entire documents or specific text segments into predefined topics or groups, essential for organizing large datasets.  Named Entity Recognition (NER): Identify and label entities like names, organizations, locations, dates, and times within text, aiding in information extraction.  Sentiment Analysis: Label text to reflect sentiments such as positive, negative, or neutral, valuable for understanding customer feedback and social media monitoring.  Question Answering and Translation: Annotate text to facilitate question-answering systems and translation tasks, enhancing multilingual support and information retrieval.  Multimodal Data Support Encord  platform is designed to handle various data types, including text, images, videos, audio, and DICOM files. It assists in centralizing and organizing diverse datasets within a single platform, simplifying data handling and reducing fragmentation.  It also assists in annotating and analyzing multiple data types and providing context and improving the quality of training data for complex AI models.  Advanced Annotation Features To enhance the efficiency and accuracy of text classification tasks, Encord provides: Customizable Ontologies: It helps in defining structured frameworks with specific categories, labels, and relationships to ensure consistent and accurate annotations across projects.  Automated Labeling: It integrates state-of-the-art models like GPT-4o to automate and accelerate the annotation process which reduces manual effort and increases productivity.  Seamless Integration and Scalability Encord platform is built to integrate smoothly into existing workflows. It allows programmatically managing projects, datasets, and labels via API and SDK access. It facilitates automation and integration with other tools and machine learning frameworks. Encord can handle large-scale datasets efficiently, supporting the growth of AI projects and accommodating increasing data volumes without compromising performance. Key Takeaways Teaching machines to read and learn through text classification involves enabling them to understand, process, and categorize text data into meaningful categories. This blog highlights the journey of text classification advancements and provides insights into key methods and tools. Here's a summary of the main points: Advancements in Text Classification: Text classification has evolved from rule-based systems and traditional machine learning methods like Naive Bayes and SVM to advanced deep learning techniques such as LSTMs, CNNs, and transformers. Impact of Pre-trained Language Models: Models like BERT, GPT, and RoBERTa have revolutionized text classification by enabling contextual understanding, bidirectional context, and scalability, making them effective for nuanced tasks like sentiment analysis and topic categorization. Transformers and Attention Mechanisms: Transformers introduced self-attention mechanisms, enabling efficient handling of long-range dependencies and improving text classification accuracy, especially for complex and lengthy texts. Practical Applications and Workflows: Modern text classification workflows utilizes pre-trained models, tokenization, and fine-tuning processes, reducing dependency on extensive labeled datasets while achieving high accuracy in tasks like sentiment analysis and spam detection. Encord’s Role in Text Classification: Encord enhances text classification workflows by offering advanced annotation tools, automated labeling with AI integration, multimodal data support, and seamless scalability, ensuring efficient and accurate NLP model development. 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.

Computer vision (CV) is driving today’s artificial intelligence (AI) advancements, enabling businesses to innovate in areas like healthcare and space. According to a McKinsey report, CV ranks second among all other AI-based solutions based on the number of applications it serves. Its rapid growth is a testament to the significant value it generates for organizations in the current era. However, with many frameworks emerging to address specific use cases, selecting the most suitable CV model for your needs can be challenging. If an ideal match is unavailable, you may need to build a custom model tailored to your requirements. In this post, we will go over state-of-the-art (SOTA) CV models across various applications and learn how you can use Encord to create your own CV solutions. Computer Vision Tasks As CV models advance, their range of tasks continues to expand. However, experts mainly classify CV tasks into three common categories: image classification, object detection, and various forms of segmentation. Image Classification Image classification assigns a predefined category or label to an input image. The goal is to determine the primary object or scene within the image. Applications include medical imaging, facial recognition, and content tagging. Image Classification Algorithms like convolutional neural networks (CNNs) and transformers are common frameworks for achieving high accuracy in classification tasks. Object Detection Object detection identifies and localizes multiple objects within an image by drawing bounding boxes around them and classifying each detected object. It combines aspects of image classification and localization. Object Detection Widely used detection models include You-Only-Look-Once (YOLO) and Faster R-CNN. They enable real-time object detection and allow experts to use them in autonomous driving, video surveillance, and retail inventory management systems. Image Segmentation Segmentation is more complex than plain classification and detection. It divides an image into meaningful regions and assigns a label to each pixel. The task includes three types: semantic, instance, and panoptic segmentation. Semantic vs. Instance vs. Panoptic Segmentation Semantic Segmentation: Assigns a class to each pixel and distinguishes between different regions in an image. It optimizes image processing in tasks like autonomous driving and medical image analysis. Instance Segmentation: Identifies and separates individual object instances within an image while assigning them a class. For example, an image can have multiple cats, and instance segmentation will identify each cat as a separate entity. Panoptic Segmentation: Unifies semantic and instance segmentation and assigns every pixel to either a specific object instance or a background class. It helps achieve efficiency in complex real-world visual tasks like robotics and augmented reality (AR). Computer Vision Applications Businesses commonly use CV deep learning models to automate operations and boost productivity. Below are examples of industries that leverage machine learning (ML) pipelines to optimize functions demanding high visual accuracy. Manufacturing Manufacturers use CV models for quality control, predictive maintenance, and warehouse automation. These models detect product defects, monitor assembly lines, and help create smart factories with autonomous robots for performing tedious tasks. Advanced CV systems can identify missing components, ensure consistency in production, and enhance safety. Additionally, they enable manufacturers to optimize maintenance schedules and extend equipment lifespan. Healthcare CV assists in diagnostics, treatment planning, and patient monitoring in healthcare. Applications include analyzing medical images like X-rays, MRIs, and CT scans to detect abnormalities like tumors or fractures. Additionally, CV enables real-time monitoring of a patient’s vital signs and supports robotic-assisted surgeries for precision and improved outcomes. Transportation As highlighted earlier, CV models form the backbone of modern autonomous vehicles, traffic management, and safety enforcement. CV systems detect objects, lanes, and pedestrians in autonomous driving. They ensure precise and safe navigation. Moreover, CV facilitates real-time traffic monitoring, optimizes flow, and identifies violations like speeding. It enables authorities to manage urban transportation infrastructure more cost-effectively. Agriculture CV models enhance crop management, pest detection, and yield estimation in agriculture. Drones equipped with CV systems monitor field conditions. They pinpoint areas that need immediate attention. The models also analyze plant health, detect diseases, and optimize irrigation. The techniques help in precision agriculture. The result is less resource waste, higher productivity, and more sustainable farming practices. Find out about the top 8 computer vision use cases in manufacturing.   Top Computer Vision Models: A Comparison The research community continually advances AI models for greater accuracy in CV tasks. In this section, we will categorize and compare various state-of-the-art (SOTA) frameworks based on the tasks outlined earlier. Image Classification Models CoCa The Contrastive Captioner (CoCa) is a pre-trained model that integrates contrastive and generative learning. It combines contrastive loss to align image and text embeddings with a captioning loss to predict text tokens. CoCa The technique generates high performance across diverse tasks, including image classification, cross-modal retrieval, and image captioning. It also demonstrates exceptional adaptability with minimal task-specific fine-tuning. PaLI The PaLI (Pathways Language and Image) model unifies language and vision modeling to perform multimodal tasks in multiple languages. PaLI It uses a 4-billion-parameter vision transformer (ViT), multiple large language models (LLMs), and an extensive multilingual image-text dataset for training. The data consists of 10B images and text in over 100 languages. PaLI achieves SOTA results in captioning, visual question-answering, and scene-text understanding. CoAtNet-7 CoAtNet is a hybrid network combining convolutional and attention layers to balance generalization and model capacity. It leverages convolution's inductive biases for generalization and attention's scalability for large datasets. A Basic Attention Layer Researchers merge convolutional and attention layers with relative attention and stack them to produce SOTA accuracy on ImageNet benchmarks. The framework offers superior efficiency, scalability, and convergence across varied data sizes and computational resources. DaViT DaViT (Dual Attention Vision Transformers) introduces a novel architecture combining spatial and channel self-attention to balance global context capture and computational efficiency. DaViT The architecture utilizes spatial and channel tokens to define the token scope and feature dimensions. The two self-attention tokens produce detailed global and spatial interactions. It achieves SOTA performance on ImageNet-1K, with top-1 accuracy of up to 90.4%. Researchers show the framework to be scalable across diverse tasks with different model sizes. FixEfficientNet FixEfficientNet enhances EfficientNet classifiers by addressing train-test discrepancies and employing updated training procedures. The FixEfficientNet-B0 variant reaches 79.3% top-1 accuracy on ImageNet using 5.3M parameters. Basic EfficientNet Architecture In contrast, FixEfficientNet-L2, trained on 300M unlabeled images with weak supervision, achieves 88.5% accuracy. The results show greater efficiency and robustness across benchmarks like ImageNet-v2 and Real Labels. Object Detection Models Co-DETR Co-DETR introduces a collaborative hybrid assignment scheme to enhance Detection Transformer (DETR)-based object detectors. It improves encoder and decoder training with auxiliary heads using one-to-many label assignments. Co-DETR The approach boosts detection accuracy and uses less GPU memory due to faster training. It achieves SOTA performance, including 66.0% AP on COCO test-dev and 67.9% AP on LVIS val. InternImage InternImage is a large-scale CNN-based foundation model leveraging deformable convolution for adaptive spatial aggregation and a large, effective receptive field. InternImage Architecture The architecture decreases the inductive bias in legacy CNNs and increases the model’s ability to learn more robust patterns from extensive visual data. It achieves 65.4 mAP on COCO test-dev and 62.9 mIoU on ADE20K. Focal-Stable-DINO Focal-Stable-DINO is a robust and reproducible object detector combining the powerful FocalNet-Huge backbone and the Stable-DETR with Improved deNoising anchOr boxes (DINO) detector. DINO Architecture The Stable-DINO detector solves the issue of multi-optimization paths by addressing the matching stability problem in several decoder layers. With FocalNet-Huge as the backbone, the framework achieves 64.8 AP on COCO test-dev without complex testing techniques like test time augmentation. The model’s simplicity makes it ideal for further research and adaptability in object detection. EVA EVA is a vision-centric foundation model designed to push the limits of visual representation at scale using public data. Experts pre-train the model on NVIDIA A100-SXM4-40GB using PyTorch-based code. EVA The pretraining task is to reconstruct image-text visual features using visible image patches. The framework excels in natural language processing (NLP) and enhances multimodal models like CLIP with efficient scaling and robust transfer learning. YOLOv7 YOLOv7 introduces a new SOTA real-time object detector, achieving optimal speed and accuracy trade-offs. It uses extended bag-of-freebies techniques, model scaling, and an innovative planned re-parameterized convolution. Basic YOLO Detection System The re-parameterization removes the identity connections in RepConv to increase gradient diversity for multiple feature maps. YOLOv7 outperforms previous YOLO models, such as YOLOv5, and achieves 56.8% AP on COCO with efficient inference.   Image Segmentation The sections below categorize segmentation models based on the semantic, instance, and panoptic segmentation tasks. Semantic Segmentation ONE-PEACE ONE-PEACE is a 4B-parameter scalable model designed for seamless integration across vision, audio, and language modalities. Its flexible architecture combines modality adapters and a Transformer-based modality fusion encoder. ONE-PEACE Architecture Experts pre-trained the framework with modality-agnostic tasks for alignment and fine-grained feature learning. The approach allows ONE-PEACE to achieve SOTA performance across diverse uni-modal and multimodal tasks, including semantic segmentation. Mask2Former Mask2Former is a versatile image segmentation model unifying panoptic, instance, and semantic segmentation tasks. It uses masked attention to extract localized features within predicted mask regions. Mask2Former It also uses multi-scale high-resolution features with other optimizations, including changing the order of cross and self-attention and eliminating dropouts. Mask2Former outperforms specialized architectures, setting new SOTA benchmarks on COCO and ADE20K for segmentation tasks. Instance Segmentation Mask Frozen-DETR Mask Frozen-DETR is an efficient instance segmentation framework that transforms DETR-based object detectors into robust segmenters. The method trains a lightweight mask network on the outputs of the frozen DETR-based object detector. Mask Frozen-DETR The objective is to predict the instance masks in the output’s bounding boxes. The technique allows the model to outperform Mask DINO on the COCO benchmark. The framework also reduces training time and GPU requirements by over 10x. DiffusionInst-SwinL DiffusionInst is a novel instance segmentation framework using diffusion models. It treats instances as instance-aware filters and formulates segmentation as a denoising process. Diffusion Approach for Segmentation The model achieves competitive performance on COCO and LVIS, outperforming traditional methods. It operates efficiently without region proposal network (RPN) inductive bias and supports various backbones such as ResNet and Swin transformers. Panoptic Segmentation PanOptic SegFormer Panoptic SegFormer is a transformer-based framework for panoptic segmentation. It features an efficient mask decoder, query decoupling strategy, and improved post-processing. Panoptic SegFormer It efficiently handles multi-scale features and outperforms baseline DETR models by incorporating Deformable DETR. The framework achieves SOTA results with 56.2% Panoptic Quality (PQ) on COCO test-dev. K-Net K-Net is a unified framework for semantic, instance, and panoptic segmentation. It uses learnable kernels to generate masks for instances and stuff classes. K-Net K-Net surpasses SOTA results in panoptic and semantic segmentation with a dynamic kernel update strategy. Users can train the model end-to-end with bipartite matching. Challenges of Building Computer Vision Models The different models listed above might create the impression that developing CV systems is straightforward. However, training and testing CV frameworks come with numerous challenges in practice. Below are some common issues developers often encounter when building CV systems. Data Quality and Quantity: High-quality and diverse datasets are essential for training accuracy. Insufficient or biased data can lead to poor generalization and unreliable predictions. Also, labeling data is labor-intensive and expensive, especially for complex tasks like object detection and segmentation. Model Complexity: CV models often comprise deep neural networks with millions of parameters. Optimizing such models demands substantial expertise, computational resources, and time. Complex architectures also risk overfitting, making it challenging to balance performance and generalization. Ethical Concerns: Ethical issues such as data privacy, bias, and misuse of CV technologies pose significant challenges. Models trained on biased datasets can perpetuate societal inequities. Improper use in surveillance or sensitive applications also raises concerns about fairness and accountability. Scalability: Deploying CV solutions at scale requires addressing computational and infrastructural constraints. Models must handle diverse real-world conditions, process data in real-time, and be adaptable to new tasks without requiring significant retraining. Encord for Building Robust Computer Vision Models Developers can tackle the above mentioned challenges by using specialized tools to streamline model training, validation, and deployment. While numerous open-source tools are available, they often lack the advanced functionality needed for modern, complex applications. Modern applications require more comprehensive third-party solutions with advanced features to address use-case-specific scenarios. Encord is one such solution. 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.  Let’s explore how Encord’s features address the challenges discussed earlier. Encord Key Features Managing Data Quality and Quantity: Encord lets you manage extensive multimodal datasets, including text, audio, images, and videos, in a customizable interface. It also allows you to integrate SOTA models in your data workflows to automate reviews, annotation, and classification tasks. Addressing Model Complexity: With Encord Active, you can assess data and model quality using comprehensive performance metrics. The platform’s Python SDK can also help build custom monitoring pipelines and integrate them with Active to get alerts and adjust models according to changing environments. Mitigating Ethical Concerns: The platform adheres to globally recognized 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 ensures data privacy using robust encryption protocols. Increasing Scalability: Encord can help you scale CV models by ingesting extensive multimodal datasets. For instance, the platform allows you to upload up to 10,000 data units at a time as a single dataset. You can create multiple datasets to manage larger projects and upload up to 200,000 frames per video at a time. 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. Learn how to use Encord Active to enhance data quality using end-to-end data preprocessing techniques.   Computer Vision Models: Key Takeaways The models discussed in this section represent just the tip of the iceberg. CV models will evolve exponentially as computational capabilities grow, unlocking new possibilities and opportunities. Below are a few key points to remember regarding CV frameworks: Best CV Models: The best SOTA models include CoCa for classification, Co-Detr for detection, ONE-PEACE for semantic segmentation, Mask Frozen-DETR for instance segmentation, and Panoptic SegFormer for panoptic segmentation. CV Model Challenges: Building robust CV models requires managing data quality and quantity, model complexity, ethical concerns, and scalability issues. Encord for CV: Encord’s data curation and annotation features can help users develop large-scale CV models for complex real-world applications.

In a rush to digitize operations, organizations are rapidly moving toward the latest artificial intelligence (AI) and machine learning (ML) frameworks to boost efficiency. A recent Forbes survey showed that six in ten companies are using generative AI (GenAI) to increase revenue. However, a Gartner poll suggests that the most significant worry of technology leaders is data privacy. The ever-increasing data volume and variety make data security challenging. This issue calls for robust data management practices to help businesses build reliable and trustworthy AI systems. One approach is to implement data visibility and traceability pipelines. These will allow developers to track and understand how AI models process raw data.  In this post, we will discuss AI data visibility and traceability, its benefits, applications, challenges, best practices, and how Encord can help optimize visibility workflows. What Does Data Visibility & Traceability Mean for AI Development? Data visibility refers to accessing and understanding data across its entire lifecycle. Traceability complements this by letting organizations track the flow and changes of vast amounts of data over time. These practices help organizations comply with ethical guidelines and legal standards during digital transformation. In addition, they enhance interpretability by fostering a deeper understanding of a model’s decision-making process. Interpretable models enable developers to see the pathways and steps a model takes to arrive at specific predictions. However, achieving this requires clarity about the model’s input data. With robust data visibility and traceability systems, developers gain insight into the types and structure of data feeding their models. This ensures high data quality for model training and provides confidence in the resulting forecasts. Benefits of AI Data Visibility & Traceability As data volume and variety increase, a robust visibility and traceability pipeline can help data-driven businesses optimize their data workflows. The list below highlights a few key benefits of making data visible and traceable. Increased Trust: Transparency into data sources and their transformations fosters trust among stakeholders. It ensures that AI systems make decisions based on high-quality and reliable data. This clarity reassures users and stakeholders by promoting confidence in AI-powered solutions. Bias Mitigation: Organizations can identify and mitigate biases in datasets by tracking data lineage. The approach promotes fairness and reduces discriminatory outcomes. Traceability also provides developers with actionable insights by pinpointing areas where biased data might influence model outcomes. Enhanced Regulatory Compliance: Traceability aids in meeting regulatory requirements by providing detailed data usage records and ensuring accountability. Such practices enhance risk management by aligning AI practices with global standards. Faster Debugging: Visibility into data flows simplifies troubleshooting and allows teams to detect and resolve issues in data pipelines more efficiently. With clear traceability, developers can prevent application downtime by quickly addressing anomalies during data collection. Data Management Optimization: Centralizing data tracking improves operational efficiency and streamlines the management of large and complex datasets. It allows experts to reduce data duplication and ensure consistency across data repositories. AI Data Visibility & Traceability Use Cases As businesses across various industries embrace AI to enhance productivity, maintaining visibility and traceability within data management systems becomes essential. The following examples illustrate how different sectors use these practices to optimize operations. Healthcare: Traceability helps verify that healthcare professionals handle patient data securely, ethically, and in compliance with industry standards. Autonomous Vehicles: Developers can track data from sensors, cameras, and other sources used to train and operate autonomous vehicles. This visibility allows them to trace decisions back to specific inputs and provides valuable insights in case of accidents or system failures. Financial Services: Financial analysts can monitor AI-driven decisions in fraud detection, credit scoring, and trading algorithms. Data traceability allows them to validate the reasoning behind predictions and detect biases in financial models. Supply Chain Management: Data visibility allows manufacturers to inspect data used in predictive analytics for managing inventory levels, demand forecasting, and logistics. It helps track product origins, monitor supplier compliance, and improve transparency in sourcing and distribution. Challenges of AI Data Visibility & Traceability While data visibility and traceability have evident advantages, implementing these practices can be complex. Teams may encounter several challenges, including: Increasing Data Complexity With multiple data types coming from diverse sources like Internet-of-Things (IoT) devices and social media, maintaining visibility is becoming difficult. Organizations must navigate this vast, heterogeneous landscape and track unstructured data accurately to maintain visibility. The evolving complexity demands advanced tools and strategies to ensure sustainability in modern AI-driven solutions. Data Silos and Fragmented Systems Isolated data repositories and disconnected systems create significant challenges for achieving visibility and traceability. Teams struggle to track data across fragmented infrastructures, resulting in inefficiencies and blind spots. Breaking down these silos requires integrated tools and processes to ensure smooth data flow and to use the power of AI for making informed decisions. AI Model Complexity In state-of-the-art (SOTA) systems like large language models (LLMs), ensuring visibility and traceability is challenging due to many parameters, nonlinear relationships, and hidden data transformations. These factors reduce interpretability and make it difficult to track how data influences outputs. Additionally, issues like overfitting and model opacity become bottlenecks in maintaining transparency in AI technologies. Data Privacy Rising concerns around data privacy and security limit access to sensitive information. Global regulations restrict how users share and analyze data. This makes tracking data origins and usage more difficult. Also, anonymization or encryption methods often obscure data. The constrained visibility prevents developers from tracking how specific data points contribute to an AI algorithm’s decisions. Scalability Tracking data flow across multiple sources, stages, and processes can become tricky as systems scale. It causes disruptions in day-to-day operations and reduces traceability. Additionally, rising data volumes can overwhelm manual tracking systems, requiring more automation to maintain accuracy and transparency at scale. Learn how Encord addresses model complexity by supporting multimodal learning   AI Data Visibility & Traceability Best Practices Organizations can address some of the challenges above by following a set of best practices. Although these practices will vary from case to case, the guidelines offer a starting point for those considering introducing visibility and traceability in their development workflows. Aligning Traceability with the Entire Data Lifecycle The data lifecycle refers to the stages data goes through, from its initial creation or collection to its eventual disposal or archiving. Aligning traceability with the data lifecycle ensures transparency and accountability at each stage. Data Lifecycle You can start by capturing relevant information about data sources, such as their origin, date of creation, and formatting details. You must also monitor data usage with robust access controls and audit logs. In addition, you should associate your ML experiments with detailed logs. These can include performance results, training and validation datasets, and algorithms deployed. Lastly, it is crucial to establish relevant key performance indicators (KPIs) and metrics to gauge the effects of visibility and traceability procedures. The approach will help developers identify areas for improvement to reduce data processing costs and time. Establish Metadata Metadata provides structured information about data, such as its source, collection date, transformation history, and usage context. You can capture metadata to track data across its lifecycle. The practice will ensure transparency, accountability, and compliance with regulatory frameworks. Comprehensive metadata also helps spot data origins, monitor changes during preprocessing, and document how it influences model training and predictions. Such traceability is vital for audits, bias detection, and debugging. It is advisable to use standardized formats and automated tools to manage metadata consistently. Additionally, metadata will contribute to your data governance efforts by enabling stakeholders to understand the data's purpose, lineage, and quality. It will also allow you to use data assets better, build trustworthy AI solutions, and quickly adapt to changing compliance frameworks. Implement Data Governance Data governance refers to the framework of policies, processes, and standards organizations establish to manage, use, and protect their data. It provides a structured approach to maintaining data quality, security, and compliance for better visibility and traceability. Data Governance Components A robust governance framework clearly defines roles and responsibilities. It assigns each team ownership of their specific datasets and ensures they are accountable for managing them effectively. It establishes data collection, storage, processing, and access guidelines to create consistent and transparent practices. Effective governance also includes regular internal audits, metadata management, and automated workflows to enforce policies and improve scalability. Create Version Control Systems Version control allows organizations to track changes to datasets, models, and code over time. It helps provide a clear record of modifications and updates. This ensures that teams can identify the exact timestamp of changes, who made them, and why they were necessary. Data Versioning Version control for datasets allows you to preserve previous versions, compare changes, and revert to earlier states if needed. For models, version control enables tracking updates in architecture, parameters, and training datasets. Together, they allow developers to trace back model results to specific data changes. You can use tools like Git or specialized data versioning systems to automate and streamline these processes. Integrating version control into workflows reduces the risk of errors, supports collaborative development, and ensures compliance with regulatory requirements. Select Robust Storage Solutions A reliable storage system securely holds data, supports efficient access, and maintains a clear record of data activity. It should accommodate large data volumes while offering scalability to meet future needs as datasets grow. These systems must support access control mechanisms to ensure that only authorized users can retrieve or modify data. Integration with tools for version control and data lineage tracking further strengthens traceability. You can opt for cloud-based storage platforms that are more flexible and scalable and have advanced features for managing data. However, on-premises solutions may be more suitable for sensitive or high-security environments. Use Data Cataloging and Lineage Tracking Tools Data cataloging creates an organized inventory of data assets that helps users quickly discover, understand, and access relevant data for their needs. In contrast, data lineage tracking maps the entire data journey, detailing its origin, transformations, and interactions with systems or processes. You can catalog and track data using specialized tools for better visibility and traceability. These tools will allow you to view your entire data ecosystem comprehensively and help members of different teams find and access datasets quickly. Continuous Monitoring Continuous monitoring evaluates data, systems, and workflows to ensure alignment with organizational goals, regulatory requirements, and performance standards. It enables real-time visibility of data pipelines, model performance, and system behavior. You can use automated tools and dashboards to facilitate continuous monitoring. The tools can consist of real-time alerts and visual insights, allowing you to address issues proactively. Training and Education Education fosters awareness of the tools and systems for monitoring data flows, transformations, and model performance. It helps teams adopt proper procedures for maintaining visibility and traceability. It also emphasizes the importance of data governance, ethical considerations, and regulatory requirements. Well-trained employees are more likely to recognize potential issues, such as data inconsistencies or unauthorized access, and take appropriate action. Additionally, continuous education helps teams stay updated on new technologies, standards, and regulatory changes. The method ensures that data traceability practices evolve with the landscape. Ultimately, training and education build a culture of accountability, supporting reliable and transparent AI systems. Data cleaning and preprocessing are key data lifecycle stages. Learn how to master in our detailed guide.   Encord for AI Data Visibility & Traceability The best practices outlined above highlight the critical need for a robust data management tool to ensure data visibility and traceability. While building a custom solution is an option, it demands significant engineering expertise and may not fully meet your evolving needs. A more practical alternative is to invest in a third-party solution that addresses the challenges of visibility and traceability while offering additional features to manage and curate complex data. One such solution is Encord, which provides comprehensive data management capabilities tailored for diverse applications. 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/multimodal data for training, fine-tuning and aligning AI models at scale. Encord Active: Evaluate and validate Al models to surface, curate, and prioritize the most valuable data for training and fine-tuning to supercharge Al model performance. Leverage automatic reporting on metrics like mAP, mAR, and F1 Score. Combine model predictions, vector embeddings, visual quality metrics and more to automatically reveal errors in labels and data. Annotation projects in Encord Key Features Handling Data Complexity: Encord handles data complexity by supporting extensive multimodal datasets, including text, audio, images, and videos, in a customizable interface. It also allows you to integrate state-of-the-art (SOTA) models in your data workflows to automate reviews, annotation, and classification tasks. Mitigating Data Silos and Fragmented Systems: The solution offers advanced features to break data silos and foster collaboration across teams. It lets you create projects and manage user roles to control how data moves across each stage in the traceability workflow. Addressing AI Model Complexity: With Encord Active, you can assess data and model quality using comprehensive performance metrics. The platform’s Python SDK can also help build custom monitoring pipelines and integrate them with Active to get alerts and adjust models according to changing environments. Ensuring Data Privacy: The platform adheres to globally recognized 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 ensures data privacy using robust encryption protocols. Maintaining Scalability: Encord can help you scale AI pipelines by ingesting extensive datasets. For instance, the platform allows you to upload up to 10,000 data units at a time as a single dataset. You can create multiple datasets to manage larger projects and upload up to 200,000 frames per video at a time. 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. AI Data Visibility and Traceability: Key Takeaways Making data processes visible and traceable is essential for building scalable AI applications. The list below highlights key points regarding data visibility and traceability. Importance of Data Visibility and Traceability: Data visibility and traceability allow organizations to track changes in extensive datasets, ensure compliance, and enhance model interpretability. Data Visibility and Traceability Challenges: High data and model complexity, fragmented systems, rising data volume, and privacy concerns make visibility and traceability difficult to implement. Encord for Data Visibility and Traceability: Encord ensures your data assets are visible and traceable throughout the data lifecycle. Book a demo now to see how Encord can simplify data visibility and traceability for your AI projects.

Data overload is a significant problem for business leaders in the current information age. According to Forbes, 90% of the data is unstructured, making it more challenging for companies to analyze and derive insights from the data they collect. This poses a significant issue for organizations that use artificial intelligence (AI) and machine learning (ML) to power their operations and products. Robust AI applications require high-quality data to deliver accurate results. However, the inability to analyze data hinders developers from implementing the right AI solutions. Data versioning is one way to address these concerns. It optimizes management and analysis by tracking and recording changes in data over time. In this post, we will discuss data versioning’s significance, challenges, best practices, and how you can use Encord to streamline your versioning pipeline. Why is Data Versioning in ML Important? Data versioning is a key element of effective ML and data science workflows. It ensures data remains organized, accessible, and reliable throughout the project lifecycle. It helps maintain consistency and reproducibility by preserving records of dataset versions. The approach allows team members to recreate experiments and enhance machine learning models. In addition, the practice facilitates efficient data management by tracking changes and organizing data systematically. The approach boosts data quality for training models and helps debug modeling issues. Versioning also improves compliance by maintaining audit trails critical for meeting regulatory standards. Lastly, it supports performance tracking by linking specific datasets to model outputs and offers insights into how data changes affect results. Challenges of Data Versioning Implementing data versioning requires extensive expertise in data engineering, data modeling, and involvement from multiple stakeholders. The list below mentions some issues data scientists may encounter when developing a scalable data version control system. Limited Storage: Versioning large datasets can quickly consume significant storage space, especially with frequent updates or high-volume data. Managing storage efficiently without sacrificing access to older versions can be costly and technically demanding. Data Management Complexity: Organizing multiple versions of datasets, associated metadata, and preprocessing scripts can overburden the infrastructure. Developers must manage dependencies between different versions of data and code carefully to avoid errors or mismatches that could compromise model performance. Security: Ensuring the security of stored data versions is an additional challenge, particularly for sensitive or regulated datasets. As new versions emerge, maintaining robust access controls and complying with data privacy laws becomes more complex. Tool Integration: Many open-source version control tools may fail to handle large, unstructured datasets. Organizations must look for specialized platforms with relevant functionality for their use case. However, integrating specialized data versioning tools into existing ML pipelines and workflows can require additional expertise and effort. Collaboration and Coordination: Managing parallel dataset changes can lead to conflicts in team settings. Effective collaboration requires clear policies and tools to handle concurrent modifications and ensure that each version of the data is consistent and accurate. Learn about the Top 6 Data Management Tools for Computer Vision   Data Versioning Approaches Organizations can overcome the challenges mentioned above by using different versioning approaches. The most common methods include: Data Duplication: Duplication is a straightforward technique that creates multiple copies of a dataset on a different machine. Users can preserve the original version in one location and make changes in another.  Data Duplication The approach works for small datasets, as duplicating large data volumes can occupy significant space. Metadata: Users can add timestamps to the existing schema, indicating the duration for which each version was relevant and active.  Metadata Versioning Including such metadata helps organizations time travel and quickly compare current and previous versions. However, as data size grows, space limitations can cause inefficiencies. Full Data Version Control: Organizations build a sustainable versioning solution as part of the native data environment using this method.  Full Version Control Full control includes associating data changes with the codebase and adding version numbers whenever modifications occur. It is compatible with all data structures and sizes and updates versions in real-time. Data Versioning Best Practices Organizations can address versioning challenges by implementing standardized procedures for creating, managing, and archiving dataset versions. While specific workflows may vary depending on the use case, adopting key best practices can enhance versioning efficiency and reliability. The following sections outline practical tips to optimize data versioning processes across diverse applications. 1. Define the Scope and Granularity Defining the scope and granularity is a foundational step in effective data versioning. Start by identifying which datasets need versioning and focus on the parts most critical to your ML workflow. Granularity will determine how you track changes. Versioning every minor update ensures detailed traceability but can be resource-intensive. On the other hand, major-change versioning simplifies management but risks overlooking important updates. Align granularity to project requirements to balance detail with practicality. Document the rationale behind versioning decisions to maintain consistency across teams. This will ensure all stakeholders understand the scope and level of detail in the versioning process. 2. Define and Track your Data Repositories A data repository is a centralized system for storing and managing datasets. It allows you to organize, access, and track all relevant data. You must structure your repositories with clear directory hierarchies to reflect dataset versions, sources, or processing stages. Organize datasets based on their specific functions to ensure clarity and prevent confusion. For example, store sales data in a dedicated directory and keep datasets for building ML models in another. Link your repositories directly to ML pipelines to streamline workflows. This integration automates the process, associating each ML experiment with its corresponding dataset. Also, you must regularly audit repositories to remove redundant or outdated versions while retaining essential ones. A systematic approach ensures data consistency, improves collaboration, and simplifies navigating large, evolving datasets. 3. Commit Changes for Easy Time-traveling In a robust version control system, commits are snapshots of the dataset at a specific point in time. They enable you to revert to earlier versions, compare changes, or troubleshoot issues. Regularly committing changes is essential for effective data versioning, as it allows for easy "time-traveling" through dataset versions. It is advisable to use descriptive commit messages to document what changed and why. This will make it easier to track updates. Plus, committing changes regularly helps maintain data traceability and reproducibility. 4. Integrate Versioning with Experiment Tracking Systems Experiment tracking systems are tools or platforms designed to record, organize, and manage all aspects of ML experiments. These systems track key components such as datasets, model configurations, hyperparameters, code versions, training metrics, and outcomes. They centralize information and help teams analyze experiment results, compare run performance, and reproduce workflows. Integrating data versioning with such systems ensures seamless coordination between datasets and ML workflows. It also enhances efficiency in collaborative projects and prevents duplication of the same datasets. Additionally, it helps maintain a clear audit trail, streamlines debugging, and enables team members to identify which changes led to model performance improvements. 5. Data Version Branching and Merging In data versioning, a user can create a branch of a primary dataset and implement changes in the branched version instead of changing the original one. Branching is crucial for managing complex datasets in ML projects, primarily when multiple team members work on the same dataset. It allows you to create separate versions of data to experiment with different preprocessing steps, feature engineering methods, or model configurations. This helps in testing variations without affecting the primary dataset. It also allows you to create isolated test environments for experimenting with new data. Merging occurs when users want to integrate the branches with the main version. During a merge, a commit is created on the target branch to combine all the changes from the forked branches, ensuring no conflicts exist.  This process keeps the original versions intact, and external users only see the changes after you merge the branch. 6. Automating the Versioning Process You can automate versioning by implementing validation checks before and after specific events in the development lifecycle. For example, Git lets you use Git hooks, which are shell scripts that run only when you trigger particular events. For instance, you can configure automated scripts to run whenever you trigger a commit. These scripts can validate the changes in the branch you are trying to merge with the main branch. They can check data integrity, verify preprocessing steps, and run tests to ensure the data does not introduce errors or inconsistencies. If the script detects an issue, it halts the commit process, preventing the main branch from becoming corrupted. This approach helps maintain the integrity of the primary dataset and ensures you only merge validated, error-free versions. 7. Defining Data Disposal Policies Defining data disposal policies is essential for maintaining data security and compliance in versioning workflows. Establish clear guidelines on when and how users should delete or archive outdated or unnecessary dataset versions. Specify retention periods based on project requirements or regulatory standards to ensure that you keep the data as long as necessary. Also, automate data disposal processes where possible, using tools to safely remove obsolete versions. This practice reduces storage costs, minimizes data clutter, and prevents unauthorized access to outdated data. 8. Naming Conventions and Metadata Standards Naming conventions should be clear, descriptive, and standardized. They should reflect the dataset's content, version, and update date. Following this practice ensures easy identification and retrieval of datasets. Metadata standards should document key information such as the data source, preprocessing steps, transformations, and model dependencies. To provide full traceability, you must Include version numbers, data lineage, and change logs. Standardizing naming and metadata practices improves data organization, enhances collaboration, and ensures team members can easily access, understand, and reproduce experiments. 9. Ensuring Data Privacy Ensuring data privacy is crucial to preventing security breaches when handling sensitive information. Implement strict access controls using role-based permissions to restrict who can view or modify specific data versions. Use encryption methods to protect data at rest and in transit, protecting it from unauthorized access. Regularly audit data versions to ensure they meet privacy regulations and apply data anonymization or de-identification techniques when needed to reduce privacy risks. 10. Selecting the Versioning Tool You must choose an appropriate versioning tool that aligns with your data and project requirements. Consider factors such as the size of your datasets, team collaboration needs, and integration with existing tools. Evaluate features such as automated version control, branching and merging support, and compatibility with cloud storage. Additionally, carefully weigh the costs and benefits of building an in-house versioning tool versus investing in a third-party solution. If you choose a third-party tool, ensure the vendor is reliable, understands the specific needs of your data, and offers strong customer support. It is also essential to assess whether the tool is user-friendly and has an active community that provides support to help you quickly get up to speed. Learn how you can Automate Training Data Quality Assessment in our detailed guide   Data Versioning using Encord As organizations accumulate more data, they must seek scalable versioning tools capable of handling diverse data types and structures. While businesses can build custom solutions, this approach requires significant expertise and resources.  Moreover, the final product may lack the essential features needed to manage datasets' evolving nature effectively. Alternatively, businesses can use specialized third-party platforms that provide comprehensive versioning and robust data management features to optimize the entire data lifecycle. One such solution is Encord, which enables efficient versioning and curation of large, unstructured datasets to meet your growing data needs. Encord is an end-to-end AI-based multimodal data management platform that helps you curate, annotate, version, and validate data for ML models. It supports image, video, audio, and text data types and offers multiple metrics to assess data quality. Encord Natural Language Search Feature Key Features Version Large Datasets: Encord helps you version and explore extensive datasets through metadata-based granular filtering and natural language search features. It can handle various data types and organize them according to their contents. Data Annotation and Collections: The platform lets you annotate and classify multimodal (video, image, audio, text, document, DICOM) data with Encord agents, allowing you to customize labeling workflows according to your use case. You can also create data collections for each project by defining collection tags according to your data type. Data Security: The platform is compliant with major regulatory frameworks, such as the General Data Protection Regulation (GDPR), System and Organization Controls 2 (SOC 2 Type 1), AICPA SOC, and Health Insurance Portability and Accountability Act (HIPAA) standards. It also uses advanced encryption protocols to protect data privacy. Integrations: Encord supports integration with mainstream cloud storage platforms such as AWS, Microsoft Azure, and Google Cloud. You can also manage workflows programmatically using its Python SDK. 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. Data Versioning: Key Takeaways Versioning datasets is no longer an optional activity. With increasing data complexity and usage, businesses must embed versioning systems within the development framework to maintain data integrity. Below are a few key points regarding data versioning. Importance of Data Versioning: Versioning allows organizations to optimize data management, traceability, and reproducibility. The technique helps streamline model experimentation and debugging. Data Versioning Challenges: Storage limitations and the complexity of managing large datasets make versioning challenging. Ensuring data privacy, integration with existing systems, and data integrity during team collaboration further complicates the process. Encord for Data Versioning: Encord is a robust data management solution that lets you version, annotate, and curate large datasets for scalable ML models.

In a world increasingly reliant on automation and artificial intelligence, Multiagent Systems are becoming essential for building complex large language models (LLMs) or multimodal models. These systems are capable of tackling challenges that are beyond the scope of a single AI agent. From coordinating fleets of autonomous vehicles to optimizing supply chains and enabling swarm robotics, these intelligent agents are transforming industries. This blog explores the core concepts, types, real-world applications, and best practices for developing effective multiagent systems, providing insights into how they enable smarter collaboration and decision-making. What are Multiagent Systems? Multiagent Systems(MAS) consist of multiple AI agents that interact within a shared environment. These systems are built to solve problems that are complex for a single agent to handle. Example of a LLM based Multiagent system. Source Core Components Agents: They are independent entities with specific objectives. They are able to understand their environment, make decisions, and execute actions to achieve their objective. E.g., software programs, or sensors. Environment: The environment is the dynamic space where agents operate. It can be physical like a factory floor or virtual like a simulation. The environment’s properties, such as accessibility and predictability, influence the agent's behavior. Communication: This allows the agents to share information and coordinate their actions. These mechanisms can be direct like message passing or indirect like modifying the environment, also known as stigmergy. Key Concepts Agent Autonomy This refers to an agent’s ability to make decisions without any external control. It involves sensing the environment, processing information, and executing actions to achieve its specific objectives. Autonomous agents improve MAS by reducing the need for centralized oversight, improving adaptability and efficiency. Decentralization Each agent operates based on local information and interactions with other agents. This design enhances the system's scalability, as new agents can be added without requiring significant reconfiguration. It also improves fault tolerance, as the failure of one agent does not compromise the entire system. Emergent Behavior This occurs when interactions among simple agents lead to complex system-wide changes that are not explicitly programmed. For example, in swarm robotics, individual robots follow basic rules, such as maintaining a certain distance from neighbors, resulting in coordinated group behaviors like flocking or obstacle avoidance. Emergent behaviors are essential for problem-solving in dynamic and unpredictable environments. Types of Multiagent AI Systems Cooperative Systems In this, agents come together to achieve a common goal. Each agent’s actions add to the collective outcome, with coordination mechanisms ensuring efficiency and conflict resolution. For example, search-and-rescue operations, where multiple drones work together to locate survivors. Competitive Systems In competitive MAS, agents have conflicting goals and aim to maximize individual outcomes, often at the expense of others. These systems are commonly seen in applications like stock trading, where agents compete for market advantage, or in adversarial game simulations. Mixed Systems Mixed MAS have both cooperation and competition. Agents might collaborate in some aspects while competing in others. For instance, autonomous vehicles may share traffic data to avoid congestion (cooperation) while simultaneously looking for optimal routes to reduce travel time (competition). Hybrid Systems This is a blend of traditional rule-based logic with adaptive learning methods. These systems allow agents to follow preprogrammed rules while using machine learning to improve the decision making process over time. For example, in a smart grid, agents may follow rules for energy distribution while learning user consumption patterns to optimize efficiency. Real World Use Cases Here are some of the multi agent-based applications in various domains: Autonomous Vehicles: Multiagent systems coordinate fleets of autonomous cars to manage traffic, optimize routes, and prevent accidents through real-time communication and decentralized decision-making. Robotics: Swarm robotics use MAS principles to deploy set of robots for tasks like warehouse automation, environmental monitoring, and disaster response. Healthcare Systems: MAS assist in patient monitoring, and resource allocation in hospitals for efficient scheduling and treatment delivery. Distributed Sensor Networks: MAS enhance environmental monitoring, surveillance, and disaster management by enabling sensors to collaborate and share data. Gaming: MAS are used in multiplayer games and simulations for realistic behavior modeling of non-player characters (NPCs) or for training purposes in defense and urban planning. Financial Systems: Automated trading platforms use multiagent systems for competitive interactions between AI agents to maximize profits and analyze market trends. Supply Chain Management: MAS optimize logistics by coordinating tasks such as inventory management, demand forecasting, and delivery scheduling across multiple AI agents. Some generative AI applications of MAS. Source Single Agent vs. Multiagent systems Single Agent Systems As the name suggests, these systems have one autonomous agent for a specific task. They are common where the environment is static and the objective is not complex and well defined. For example, recommendation systems. Multiagent Systems These distributed systems have more than one autonomous agent in a shared environment. Each agent can either have its own specific goal or work with other agents towards a collective goal. Example, drones working together to survey an area, or autonomous bidding agents in auctions. Source Challenges in Training Multiagent AI Systems It can be tricky training multi-agent systems since there are different agents interacting with each other in the same environment. Here are some of the common challenges: Scalability: As the number of agents increases, the computational need for communication between agents also increases. Dynamic Environments: Each agent’s actions changes the ecosystem. Now these constant changes and external factors make it difficult to predict outcomes or develop consistent strategies. Credit Assignment: Each agent’s actions are accounted for. Determining which agent’s actions led to success or failure is challenging especially in cooperative tasks where contributions are added up. Communication Bottlenecks: Agents often rely on communication to coordinate, but limited bandwidth, high latency, or long and complex messages can slow down decision making. Evaluation Metrics: Measuring the performance of multi-agent systems is complex, as it must account for individual agent goals, overall system efficiency, and fairness among agents. How Encord Supports Multiagent System Development Encord is a data annotation platform designed to support the training of machine learning models and multiagent systems. It provides tools to manage and curate multimodal datasets. It helps with large-scale data annotation, designing workflows, and integrating it into machine learning pipelines. Here are some of the key features of Encord that help in building MAS: High-Quality Annotated Data: With support for all modalities, features like ontology, and tools like Encord Active to visualize, and quality metrics to find labeling mistakes, this platform can handle complex data annotation while ensuring precision. Scalability and Efficiency: Training multiagent systems often requires managing large amounts of data. Encord is built to scale, allowing you to work with large datasets that are necessary for effective training. It also supports parallel annotation pipelines, allowing multiple tasks to run at once, which speeds up the process of preparing data for training. Effective Collaboration: With custom workflows, the platform makes it easy for distributed teams to work on data annotation.  Practical Steps to Build Effective Multiagent Systems Define Objective of Each Agent For building a multiagent system, the first step is to assign each agent with specific goals and responsibilities. Whether agents are cooperating, competing, or performing independent tasks, their objectives should be clearly outlined. The goal of the overall system should also be defined in order to assign tasks to each agent and to calculate the number of agents required. Design Environment and Interaction Rules The ecosystem  where the agents are to interact should be created next. This includes defining how the agents interact with each other, the environment, and the set of rules that govern these interactions.  Choose Learning Algorithm Here, select the learning algorithm based on the objective of the system. If the agents need to collaborate, multi agent reinforcement learning or MARL algorithms like QMIX can be chosen. For competitive scenarios, consider algorithms that can handle adversarial behaviors like Nash equilibrium. Annotate and Simulate Cure and annotate the data for training that reflects the real world scenario in which the agents will operate. Using tools like Encord can help in data curation, management, and annotation of high quality training and testing data. This is important for building agents that can handle complex tasks and dynamic environments. Train the Agents Once the environment and data are set up, begin training the agents. Use AI to allow the agents to learn real-time decision making from their interactions and experiences. This is where the real learning happens, as agents adjust their behavior based on rewards and punishments. Automate your data pipelines with Encord Agents to reduce the time taken to achieve high-quality data annotation at scale. Test and Iterate Testing is important to evaluate how well the agents are performing. Simulate different scenarios close to real world scenarios to see how the agents respond, and adjust the rules, training data, or the learning algorithm. Deploy and Monitor After training and testing, deploy the MAS in a real-world or production ecosystem. Monitor the system’s performance regularly to ensure the agents are behaving as expected. For more information, read the blog AI Agents in Action: A Guide to Building Agentic AI Workflows. Popular Learning Algorithms Used in Multiagent Systems Multiagent Reinforcement Learning(MARL) MARL is a key approach in multiagent systems where agents learn by interacting with the environment and the other agents. Here, each agent receives feedback based on its actions and the environment like in RL. The objective of the overall system is to maximize individual or group rewards over the time by improving the interaction rules. Common MARL Algorithms Independent Q-Learning (IQL): In this each agent treats other agents as part of the environment and learns independently using Q-learning. IQL struggles in environments with many agent interactions. Proximal Policy Optimization (PPO): It is a RL algorithm that focuses on policy or rule optimization. It works well in both cooperative and competitive environments and is used in training agents in multi-agent scenarios like games or robotics. QMIX: This is a centralized training approach for multi-agent systems where a global reward function is used to train the agents individually. QMIX is designed to handle environments where agents work together toward a shared objective. If you want to implement some of these algorithms, check out this GitHub repo. Centralized Training with Decentralized Execution (CTDE) CTDE is a strategy used to train agents in a cooperative environment while ensuring that each agent acts independently during execution. The main idea behind it is to have a centralized controller that overlooks the training and helps the systems learn the necessary agent behaviors. However, during actual operation, agents rely on their local observations to make decisions. Common CTDE Algorithms Multi Agent Deep Deterministic Policy Gradient: In this algorithm, during training agents have access to the observations of all agents but during execution, each agent uses only its own observations to make decisions. This works well for a collaborative approach. Value Decomposition Networks(VDN): This approach decomposes the global value function into individual value functions, making it easier for agents to cooperate without requiring a complex global reward structure. It is particularly useful in environments where agents need to act as a team but do not have direct communication with each other during execution. Game Theory Based Algorithms Game theory is a mathematical framework for analyze interactions between agents with conflicting interests. In MAS, this algorithm helps agents to make strategic decisions when they are in adversarial conditions. Common Game Theory Algorithms Nash Equilibrium: In competitive scenarios, a Nash equilibrium represents a set of strategies where no agent can improve its payoff by unilaterally changing its own strategy. The agents use this algorithm to predict how their competitors will behave and adjust their actions and rules accordingly. Fictitious Play: This iterative algorithm allows agents to learn and adapt to the strategies of other agents over time. In each iteration, agents update their strategies based on the belief about the opponent's strategy. Swarm Intelligent Algorithms(SIA) SIAs are a class of search algorithms that are inspired by the collective behaviour of decentralized systems, like birds flocking. These algorithms allow agents to collaborate in a distributed manner, and solving complex problems without a centralized control. Common SIAs Particle Swarm Optimization(PSO): In this technique, the agents simulate the social behaviour of birds to achieve the adjective. Each agent adjusts its position based on its previous experience and the best solution found by the group. It is mostly used in route planning in traffic flow. Best Practices for Building Multiagent Systems Here are some of the tips to keep in mind when implementing multiagent systems: Design a Realistic and Adaptable Environment Make sure to build the environments which mimic the real world conditions the agents will use. This will help the agents to learn how to behave in unpredictable scenarios better. Platforms like Unity can be used to simulate complex environments for testing. Use Scalable Communication Strategies The agent communication methods should be efficient, minimal and scalable. Unnecessary communication protocols can cause computational overload when the number of agents are increased. Robust Credit Assignment Mechanisms Identify which agent actions lead to success or failure using credit assignment methods like Shapley Value. This ensures fair rewards and accountability in agent collaboration tasks. Efficient Data Annotation Tools Use annotated datasets that capture agent interactions and environment complexity. Tools like Encord streamline dataset preparation, improving training efficiency. Prioritize Ethical and Safe Deployments Ensure agents follow ethical and safety guidelines, especially in critical areas like healthcare or autonomous vehicles. Safeguards help prevent unintended or harmful behaviors. Conclusion Multiagent systems(MAS) offer powerful solutions for complex problems. They use autonomous agents to work together or independently in dynamic environments. Their applications span industries like robotics, healthcare, and transportation, showing their advancements in adaptability and scalability. By defining clear objectives, designing realistic environments, and with tools like Encord for efficient data preparation, developers can create systems that are both effective and ethical. Start building multiagent systems today and explore their potential in solving real-world challenges.

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.