Awesome GitHub RepositoriesStructured Document Extraction

PaddleOCR is a comprehensive optical character recognition framework designed for detecting and transcribing text from images and documents into structured, machine-readable formats. It provides a modular computer vision pipeline that decouples image preprocessing, text detection, and character recognition into independent, configurable stages. This architecture supports automated document digitization and multilingual text recognition, capable of identifying text in over one hundred languages across diverse environments ranging from scanned documents to industrial scenes. The framework disti

MinerU is a document parsing pipeline designed to transform unstructured files into machine-readable, structured data. It utilizes deep learning models to perform layout analysis, identifying document regions and extracting complex content such as mathematical expressions. By combining these neural network inferences with geometric heuristics, the system reconstructs the reading order and structural hierarchy of documents to ensure accurate data representation. The project distinguishes itself through a multi-stage processing workflow that integrates layout detection, optical character recogn

PageIndex is an agent-ready knowledge engine that processes documents into hierarchical tree structures to enable reasoning-based information retrieval. By organizing content into logical trees rather than relying on traditional vector database chunking, the platform preserves the original structure and flow of complex documents. It functions as a Model Context Protocol server, allowing external AI agents to connect to and query indexed knowledge bases through standardized communication protocols. The platform distinguishes itself by using vision-language models to process raw document images

This project is a high-performance headless browser engine designed for scalable web automation, data extraction, and AI agent integration. It provides a specialized environment that allows autonomous agents and testing frameworks to interact with web content through standardized remote control protocols. By executing pages in a lightweight, headless state, the engine minimizes resource consumption while maintaining the ability to perform complex navigation and dynamic content rendering. The platform distinguishes itself through deep integration with AI-centric communication layers and advanc

This project is a PDF data extraction tool and document preprocessor designed to convert PDF files into structured formats such as Markdown, JSON, and HTML. It functions as an OCR document parser for scanned files, an accessibility automator for generating PDF/UA compliant metadata, and a loader for AI orchestration frameworks like LangChain. The software distinguishes itself through specialized handling of complex document elements, including the conversion of mathematical formulas into LaTeX and the generation of natural-language descriptions for charts and images. It utilizes recursive seg

Kotaemon is an orchestration framework designed for building modular, agentic workflows that integrate document processing, retrieval-augmented generation, and multi-step reasoning. It provides a comprehensive platform for developing document-based question answering systems, allowing users to chain language models, prompt templates, and external tools into complex, automated pipelines. The system distinguishes itself through a highly modular architecture that emphasizes component-based composition and schema-driven data exchange. It supports autonomous agents capable of decomposing complex q

This project is a comprehensive framework and toolkit for developing, optimizing, and deploying transformer-based models across multimodal, document intelligence, and natural language processing tasks. It provides a unified neural architecture that processes text, vision, audio, and document layout data through a shared set of weights, enabling researchers and developers to build foundational models that align cross-modal representations. The platform distinguishes itself through advanced training and inference strategies designed for large-scale deep learning. It incorporates specialized mec

Qwen2.5-VL es un transformador multimodal autorregresivo diseñado para procesar secuencias intercaladas de tokens de texto y visuales. Integra incrustaciones de características visuales en un espacio de modelo de lenguaje compartido para realizar razonamiento intermodal y generar respuestas coherentes o código de diseño estructurado. El proyecto se distingue por el mapeo de visión-lenguaje-acción, lo que le permite percibir interfaces visuales y traducir esa percepción en comandos accionables para operar pantallas digitales y hardware robótico. Emplea codificación de imagen de resolución dinámica y indexación de video de fotogramas temporales para manejar diversos tamaños de imagen y secuencias visuales de larga duración. El modelo cubre una amplia superficie de capacidades, incluyendo reconocimiento óptico de caracteres multilingüe para la digitalización de documentos, conexión espacial para localizar objetos a través de cuadros delimitadores y el análisis de contenido de video de larga duración. También admite razonamiento matemático multimodal para resolver problemas utilizando gráficos y diagramas, y extiende su comprensión a una longitud de contexto de un millón de tokens.

Qwen2-VL is a multimodal large language model and vision language model designed to process and reason across text, images, and video content. It functions as a visual reasoning engine and a visual agent framework, capable of interpreting visual data to perform object detection, document parsing, and spatial reasoning. The model is distinguished by its ability to act as a video understanding model, processing hour-long videos with second-level indexing and event recall. It further differentiates itself through a visual agent capability that interacts with software interfaces and robotic hardw

Qwen3-VL is a multimodal vision-language model designed to process and reason across images, videos, and text. It functions as a computer vision framework capable of identifying objects, extracting structured data from documents, and interpreting spatial elements within visual media. The system operates as an automated user interface interaction agent, interpreting screen data to navigate software and mobile applications. By utilizing a unified transformer architecture, it performs complex visual reasoning to execute user-defined tasks without manual input. Beyond interface navigation, the m

Unstructured is an enterprise-grade data orchestration engine designed to transform raw, unstructured files into structured, machine-readable formats. It functions as a comprehensive platform for document ingestion, partitioning, and enrichment, specifically engineered to prepare complex data for retrieval-augmented generation and agentic AI workflows. The platform distinguishes itself through its sophisticated document processing strategies, which combine rule-based extraction with vision-language models to handle diverse file layouts, tables, and images. It provides a modular architecture t

Zerox is a multimodal document parser and OCR tool that uses vision models to convert PDF files and images into structured Markdown text. It functions as a visual layout extraction engine, leveraging large multimodal models to digitize documents while maintaining their original structural formatting. The system differentiates itself through the use of coordinate-based element mapping and multimodal layout analysis to identify structural elements like tables, charts, and headers. It utilizes rasterization to convert vector PDF pages into high-resolution bitmaps, ensuring consistent input for t

A fast, helpful, and open-source document parser

PyMuPDF is a comprehensive PDF manipulation library and document analysis tool. It serves as a text extraction tool, OCR engine, and image converter, providing a programmatic interface to edit, merge, split, and optimize PDF and Office documents. The project distinguishes itself through high-performance capabilities, including the use of C-bindings for low-level manipulation and parallelized page processing to accelerate workloads. It provides specialized conversion paths, such as transforming PDF content into Markdown for retrieval-augmented generation and large language model pipelines. It

Dolphin is a multimodal layout analyzer and image-to-structure converter that transforms photographed or digital document images into machine-readable structured data. It functions as an LLM document parser, utilizing vision-language models to simultaneously predict spatial layout and text content. The system is designed as a concurrent document processor, employing parallel document parsing to process multiple elements across distributed compute nodes. This high-throughput approach reduces the total time required to convert large volumes of images into structured formats. The project covers

Kreuzberg is a document extraction engine that converts PDFs, Office files, images, and over 90 other formats into clean, structured text and metadata. It is built around a compiled Rust core that can be used as a native library, a command-line tool, a REST API server, or a WebAssembly module for browser-based processing. The system is designed to run entirely on self-hosted infrastructure, with no data leaving the user's environment. What distinguishes Kreuzberg is its breadth of integration surfaces and its pipeline architecture. It exposes extraction capabilities through native bindings fo

DocTR is a deep learning OCR library built on PyTorch that detects and transcribes text in document images using a two-stage detection-recognition pipeline. It provides a complete framework for building and deploying OCR pipelines with pretrained models available through the Hugging Face Hub, and supports exporting trained models to ONNX format for cross-runtime deployment. The library offers end-to-end OCR pipelines that combine text detection and recognition to extract all text from document images or PDFs, with support for rotated page handling and varied text orientations. It includes cap

Layout-parser es un framework de deep learning para el análisis de diseño de documentos y análisis de imágenes. Proporciona un kit de herramientas para extraer información estructural y patrones de diseño de documentos escaneados e imágenes digitales, transformándolos en estructuras de datos programáticas para el análisis automatizado. El framework integra la detección de diseño con reconocimiento óptico de caracteres (OCR) para convertir regiones tabulares en datos legibles por máquina. Utiliza redes neuronales para identificar y clasificar elementos estructurales dentro de imágenes de documentos sin depender de sistemas manuales basados en reglas. El sistema cubre una amplia gama de capacidades de análisis de documentos, incluyendo el análisis de estructura de documentos, extracción automatizada de tablas y representación jerárquica de diseño. También incluye herramientas de visualización para renderizar elementos detectados y jerarquías sobre imágenes originales para la verificación de resultados.

Sparrow es una plataforma de extracción de documentos basada en LLM y un motor de inferencia visual diseñado para convertir imágenes y PDFs en datos estructurados validados. Funciona como un orquestador de flujos de trabajo agenticos que encadena tareas de clasificación, extracción y validación en pipelines de múltiples pasos. El sistema se distingue por una capa de inferencia agnóstica al backend que gestiona modelos en GPUs locales, Apple Silicon y proveedores en la nube. Emplea grounding visual basado en coordenadas para mapear el texto extraído a coordenadas precisas de cuadros delimitadores y utiliza dirección de modelos basada en pistas para guiar la atención y normalizar formatos de datos. La plataforma cubre flujos de trabajo de inteligencia documental, incluyendo procesamiento especializado de tablas basadas en imágenes para mantener la integridad estructural y validación basada en esquemas para verificar la exactitud de los campos extraídos. También proporciona un panel de análisis documental para monitorear el rendimiento de la API, analíticas de uso y el estado del sistema. La arquitectura incluye un sistema de extensión basado en plugins para integrar librerías de terceros utilizadas en indexación y orquestación.

sChandra is a document processing platform that converts images, PDFs, Word documents, spreadsheets, and other formats into structured output such as HTML, Markdown, or JSON while preserving layout. It can also extract specific data fields from invoices, contracts, or reports using user-defined JSON schemas, with citations back to source locations. The service supports form filling in PDF and image documents, document generation from Markdown, and extraction of tracked changes from Word files. The platform distinguishes itself with pipeline-based processing chains that combine multiple proces