Easy-to-use and powerful LLM and SLM library with awesome model zoo.

Document intelligence framework for Python - Extract text, metadata, and structured data from PDFs, images, Office documents, and more. Built on Pandoc, PDFium, and Tesseract.

A collection of original, innovative ideas and algorithms towards Advanced Literate Machinery. This project is maintained by the OCR Team in the Language Technology Lab, Tongyi Lab, Alibaba Group.

ContextGem: Effortless LLM extraction from documents

A curated list of resources for Document Understanding (DU) topic

ExtractThinker is a Document Intelligence library for LLMs, offering ORM-style interaction for flexible and powerful document workflows.

AI-in-a-Box leverages the expertise of Microsoft across the globe to develop and provide AI and ML solutions to the technical community. Our intent is to present a curated collection of solution accelerators that can help engineers establish their AI/ML environments and solutions rapidly and with minimal friction.

ReadingBank: A Benchmark Dataset for Reading Order Detection

A collection of samples demonstrating techniques for processing documents with Azure AI including AI Foundry, OpenAI, Document Intelligence, etc.

The Doc Intelligence in-a-Box project leverages Azure AI Document Intelligence to extract data from PDF forms and store the data in a Azure Cosmos DB. This solution, part of the AI-in-a-Box framework by Microsoft Customer Engineers and Architects, ensures quality, efficiency, and rapid deployment of AI and ML solutions across various industries.

This sample demonstrates how to use Document Intelligence's Layout model to convert a PDF document, such as invoices, into Markdown, then use GPT-3.5 Turbo to extract structured JSON data using the Azure OpenAI Service.

A curated list of resources on Table Structure Recognition

BoundaryNet - A Semi-Automatic Layout Annotation Tool

AI-powered document intelligence platform for automated analysis, processing, and insights extraction from various document formats.

An experiment to provide the capabilities of Azure AI Document Intelligence Studio template training for feedback loop

A curated list of resources on Document Layout Analysis

An explainable AI system that combines Graph Intelligence, Vector Search, and Retrieval-Augmented Generation (RAG) to deliver grounded answers and transparent reasoning paths. Includes a FastAPI backend, Streamlit UI, FAISS vector index, and an in-memory knowledge graph for hybrid retrieval and recommendations.

Using Azure Document Intelligence and Azure OpenAI services to automatically extract data from invoices.

Azure AI Samples

A collection of solutions that leverage Azure AI services.