opendatalab/PDF-Extract-Kit

Health v2 · maintenance only

Adoption not part of the score

9993 stars · 750 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

PDF-Extract-Kit is a Python model toolbox for high-quality PDF content extraction, integrating state-of-the-art models for layout detection, formula detection and recognition, OCR, table recognition, and reading order. Its modular design lets developers combine components to build applications like document translation or Q&A, while end users seeking PDF-to-Markdown conversion are pointed to the companion MinerU tool.

Use cases

• extract text and structure from complex pdf documents
• convert scanned pdfs to markdown with ocr
• detect and recognize math formulas in pdfs
• recognize tables in pdf documents
• detect document layout regions in pdfs
• build document qa or translation apps on top of pdf parsing models
• benchmark pdf parsing models on evaluation datasets

When to choose

• you need modular, model-level building blocks for document parsing tasks
• you want state-of-the-art layout, formula, OCR, and table recognition models with benchmarks
• you are a developer building custom document processing applications
• you need fine-grained control over individual parsing stages

When to avoid

• you just want a turnkey pdf-to-markdown converter - use MinerU instead
• you need a simple text extraction utility without deep learning models
• you cannot run GPU or heavyweight ML models in your environment
• your project requires a permissive license - this is AGPL-3.0

Facets

library · maturity active

ocr pdf machine-learning parser image-processing pdf machine-learning developer-tools python windows pdf-extraction layout-detection formula-recognition table-recognition document-parsing model-toolbox natural-language-processing linux macos gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="opendatalab/PDF-Extract-Kit")