What Is MinerU? MinerU 是什么？
MinerU is an open-source project with 74k+ GitHub stars. Licensed under Apache-2.0. High-quality document parser converting PDFs to Markdown
The project focuses on document, pdf, parsing use cases and is designed as a ready-to-use application—you can deploy or run it directly without writing integration code.
Source code is available at github.com/opendatalab/MinerU. With 74k+ GitHub stars, it ranks among the most battle-tested open-source tools in this space—meaning most common use cases are well-documented with community solutions available.
Extract structured data from financial reports or research papers at scale—MinerU's 95% accuracy on complex layouts beats manual processing or fragile regex scripts. Unlike PyPDF2's 70% accuracy rate, it preserves formatting and tables natively. Skip it if you need real-time streaming parsing or handle proprietary binary formats; the 74k+ stars reflect strength in static document conversion only.
Extract structured data from financial reports or research papers at scale—MinerU's 95% accuracy on complex layouts beats manual processing or fragile regex scripts. Unlike PyPDF2's 70% accuracy rate, it preserves formatting and tables natively. Skip it if you need real-time streaming parsing or handle proprietary binary formats; the 74k+ stars reflect strength in static document conversion only.
— AI Nav Editorial Team
Who Should Use MinerU? 谁适合使用 MinerU？
✓ Good Fit For适合以下场景
- Developers and end users who want to use AI capabilities quickly without building integrations from scratch
- Teams that need a ready-to-use UI interface
✕ Not Ideal For不适合以下场景
- Pure backend engineering scenarios requiring deep API customization (framework libraries are a better fit)
Key Features 核心功能
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Table & Formula Preservation — Extracts and reconstructs tables, mathematical formulas, and multi-column layouts as valid Markdown, maintaining structural integrity lost in standard PDF extraction.
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95% Accuracy on Structured PDFs — Achieves ~95% conversion accuracy on structured documents versus ~70% for PyPDF2, reducing manual correction overhead for enterprise document workflows.
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Batch CLI Processing — Process hundreds of PDFs simultaneously via command-line interface, enabling scalable document conversion pipelines without iterative single-file handling.
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Layout-Aware Markdown Output — Intelligently detects and preserves document structure including headers, footers, columns, and text hierarchy in semantically correct Markdown format.
Pros & Cons 优缺点
✓ Pros优点
- High-quality PDF to Markdown conversion achieving ~95% accuracy on structured PDFs (vs ~70% for PyPDF2)
- Preserves tables, formulas, and multi-column layouts that naive text extraction destroys
- Supports batch processing of hundreds of PDFs via CLI
✕ Cons缺点
- GPU inference requires 4GB+ VRAM for acceptable speed; CPU mode is ~5-10x slower
- Complex scanned PDFs with low-resolution images require OCR preprocessing for best results
- Output quality depends heavily on source PDF quality — poorly formatted PDFs still produce messy output
Use Cases 应用场景
MinerU is used across a wide range of applications in the AI development ecosystem. Here are the most common scenarios where teams choose MinerU:
📄 PDF-to-Markdown Conversion
Convert complex PDFs with tables, formulas, images, and multi-column layouts into clean, structured Markdown that preserves document hierarchy.
🔬 Scientific Paper Parsing
Extract text, LaTeX formulas, figures, and tables from academic papers—output structured JSON ready for RAG ingestion or knowledge graph construction.
📚 Enterprise Document Digitization
Batch-process scanned contracts, invoices, and reports into searchable, structured formats for downstream AI processing and compliance archiving.
Getting Started with MinerU MinerU 快速开始
pip install magic-pdfmagic-pdf --helpSimilar AI Tools 相似 AI 工具
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