document-intelligence
There are 13 repositories under document-intelligence topic.
- PaddleNLP
PaddlePaddle / PaddleNLP
Easy-to-use and powerful LLM and SLM library with awesome model zoo.
Language:Python 12839 Goldziher / kreuzberg
Document intelligence framework for Python - Extract text, metadata, and structured data from PDFs, images, Office documents, and more. Built on Pandoc, PDFium, and Tesseract.
Language:HTML 2498AlibabaResearch / AdvancedLiterateMachinery
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.
artificial-intelligencedocumentaimultimodalmultimodal-deep-learningocrcomputer-visionvision-language-transformerend-to-end-ocrscene-text-detectionscene-text-detection-recognitionscene-text-recognitiontext-detectiontext-recognitionvision-languagedocumentdocument-analysisdocument-recognitiondocument-understandingdocument-intelligencevision-language-modelLanguage:C++ 1796- contextgem
shcherbak-ai / contextgem
ContextGem: Effortless LLM extraction from documents
Language:Python 1706 tstanislawek / awesome-document-understanding
A curated list of resources for Document Understanding (DU) topic
awesome-listmachine-learninginformation-extractionkey-information-extractiondocument-understandingrobotic-process-automationdocument-analysisdocument-layout-analysisocrnatural-language-processingdeep-learningnlpawesomepdfrpapdf-documentsdocument-intelligenceunstructured-dataintelligent-processingdocument-ai- ExtractThinker
enoch3712 / ExtractThinker
ExtractThinker is a Document Intelligence library for LLMs, offering ORM-style interaction for flexible and powerful document workflows.
Language:Python 1455 Azure / AI-in-a-Box
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.
Language:Jupyter Notebook 588Azure-Samples / azure-ai-document-processing-samples
A collection of samples demonstrating techniques for processing documents with Azure AI including AI Foundry, OpenAI, Document Intelligence, etc.
Language:Bicep 113doc-analysis / ReadingBank
ReadingBank: A Benchmark Dataset for Reading Order Detection
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.
Language:Bicep 40jamesmcroft / azure-document-intelligence-markdown-to-openai-data-extraction-sample
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.
Language:Jupyter Notebook 31qyhou / curated-table-structure-recognition
A curated list of resources on Table Structure Recognition
ihdia / BoundaryNet
BoundaryNet - A Semi-Automatic Layout Annotation Tool
Language:Python 24AI-Engineering-Study-Group / docugent
AI-powered document intelligence platform for automated analysis, processing, and insights extraction from various document formats.
Language:Python 17AmirhosseinHonardoust / Graph-RAG-Engine
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.
Language:Python 13An experiment to provide the capabilities of Azure AI Document Intelligence Studio template training for feedback loop
Language:Python 10A curated list of resources on Document Layout Analysis
joinalahmed / invoiceparsingwithAOAI
Using Azure Document Intelligence and Azure OpenAI services to automatically extract data from invoices.
Language:HTML 3aihearticu / azure-ai-learning-hub
Comprehensive learning hub for Azure AI services - 130+ labs and tutorials covering AI-102 certification
Language:Python 1BryanTheLai / StackRAG-Backend
StackRAG is a multi-tenant Retrieval-Augmented Generation (RAG) platform for financial document intelligence. It extracts structured data from financial PDFs using LLMs, offers secure multi-tenancy, real-time APIs, and is built on Python, FastAPI, Docker, and PostgreSQL.
Language:Jupyter Notebook 1codedbyasim / Generative-AI-Document-Intelligence-System
Extract and summarise data from PDFs and images using OCR + LLMs. Built with Python, OpenCV, HuggingFace, and Flask.
Language:Python 1fenilsonani / rag-document-qa
Enterprise-grade RAG system featuring dual online/offline operation, multi-modal document processing, and advanced AI capabilities including knowledge graph construction and hybrid search for intelligent document analysis.
Language:Python 1FrancescoRomeo02 / multimodalragApp
Advanced multimodal RAG system for querying PDF documents with text, images, and tables using vector embeddings, semantic chunking, and LLMs via Groq API
Language:Python 1fri3erg / DataDig-AIExtractor
App used to extract structured data from documents photos or pdfs via custom templating and commercial LLM (GPT and Azure Document Intelligence). Developed as a Computer Science Thesis at University of Bologna
Language:Python 1igorcervac / AzureAiSamples
Azure AI Samples
Language:C# 1kanugurajesh / DocuMind
DocuMind is a document intelligence app where users can upload files, extract knowledge, and query them in natural language, combining semantic search (Qdrant), graph insights (Neo4j), and LLM reasoning.
Language:TypeScript 1ks6088ts-labs / azure-ai-services-solutions
A collection of solutions that leverage Azure AI services.
Language:Python 1Md-Emon-Hasan / AutoDocThinker
Agentic AI system that allows users to upload documents (PDFs, DOCX, etc.) and natural language questions. It uses LLM-based RAG to extract relevant information. The architecture includes multi-agent components such as document retrievers, summarizers, web searchers, and tool routers — enabling dynamic reasoning and accurate responses.
Language:Jupyter Notebook 1mmTheBest / AI-Agents-for-Business
A live, evolving collection of open-source AI agents and real examples showing how businesses can use AI to automate work, save time, and explore new ideas.
MSUSAzureAccelerators / SouthReusableAssets
IP and use case assets for CSU
Language:Ruby 1sshashi7 / AIRoadShow
Hands-on labs and mini hackathon to build a Sales Buddy Agent using Copilot Studio and Azure AI
Sumanth1410-git / internal-docs-agent
Enterprise AI assistant for intelligent document Q&A via Slack - Advanced RAG system with multi-language support.
Language:Python 1willermo / markdown-for-llms
A comprehensive, production-ready Python pipeline for converting various document formats into clean, validated, and optimally chunked Markdown files ready for Large Language Model (LLM) consumption and NotebookLM notebooks.
Language:Python 1JPatronC92 / contextaline
🔍 AI-powered document search with semantic understanding. Find files by content using Sentence-BERT. Modern PyQt6 GUI with keyboard shortcuts, search history, and context menu. Supports PDF, DOCX, TXT. 92% precision with AI.
Language:Pythonkhushiiagrawal / Advanced_Graph_RAG
This project answers natural-language questions over your Excel inventory and business PDFs using a hybrid RAG pipeline. It combines semantic embeddings (FAISS) with BM25 for exact IDs, extracts structured fields (e.g., totals, GST) from PDFs and builds an explainable relationship graph; results can be exported to Neo4j for graph exploration.
Language:PythonMrSpecks / AI-Document-Intelligence-Platform-v1
The AI Document Intelligence Platform is an enterprise-oriented MVP that automates extraction, analysis, and summarization of business documents.