Unlock the Power of Your PDFs: A Deep Dive into MinerU for Enhanced Data Extraction

Tired of wrestling with PDFs? MinerU, an open-source solution, is here to revolutionize how you extract data from your documents. Convert PDFs into machine-readable formats like Markdown and JSON, unlocking insights and streamlining your workflows. Read on to see how MinerU can dramatically improve your PDF data extraction process.

What is MinerU and Why Should You Care?

MinerU streamlines PDF processing, allowing you to effortlessly extract key information. Born from the InternLM project, it tackles complex symbol conversion challenges in scientific literature.

MinerU aims to be a game-changer, offering a powerful alternative to commercial solutions for extracting data from PDFs.

Key Features of MinerU: Automate Your PDF Workflow

MinerU offers a comprehensive suite of features to simplify PDF data extraction:

• Semantic Coherence: Removes headers, footers, and page numbers, ensuring clean, meaningful text.
• Intelligent Layout Handling: Accurately outputs text in logical reading order, even in complex multi-column layouts.
• Structural Preservation: Retains document structure including headings, paragraphs, and lists.
• Rich Media Extraction: Pulls out images, descriptions, tables, and footnotes.
• Formula Conversion: Automatically recognizes and converts formulas to LaTeX format.
• Table Conversion: Converts tables to HTML format for easy use.
• OCR Capabilities: Automatically detects and enables OCR for scanned or garbled PDFs, supporting 84 languages.
• Multiple Output Formats: Exports data in Markdown, JSON, and other formats.
• Visualization Tools: Offers layout and span visualization for output quality confirmation.
• Cross-Platform Compatibility: Runs on Windows, Linux, and Mac, with CPU and GPU (CUDA/NPU/MPS) support.

Installation and Compatibility: Get MinerU Up and Running

MinerU prioritizes broad compatibility. Recent updates show:

• Python Support: Compatible with Python versions 3.10-3.13.
• CUDA Compatibility: Supports CUDA versions 11.8/12.4/12.6/12.8.
• Offline Deployment: No internet connection needed after initial deployment.

Important Considerations: While MinerU strives for wide compatibility, best performance is guaranteed on recommended hardware and software configurations.

Performance Optimization: Faster and More Efficient PDF Processing

Significant performance enhancements have been implemented:

• Batch Processing: Supports batch processing of multiple PDF files for faster overall parsing.
• Memory Optimization: Reduced GPU memory usage, requiring a minimum of 6GB.
• Hardware Acceleration: Improved running speed on MPS devices.

These optimizations enable faster and more efficient extraction of data from PDFs.

Parsing Improvements: Enhanced Accuracy

The mfr model has been updated, resolving issues such as lost line breaks in multi-line formulas. Also, paddleocr2torch replaces the paddle framework, addressing compatibility and thread safety concerns.

Quick Start: Unleash MinerU's Potential Immediately

Jump into MinerU with these options:

• Online Demo: (No Installation Needed) Try a quick test run.
• Quick CPU Demo: (Windows, Linux, Mac)
  1. Install magic-pdf: pip install -U "magic-pdf[full]"
  2. Download model weight files (refer to documentation).
  3. Modify the configuration file (magic-pdf.json) to enable/disable features.
• Accelerated Inference: Leverage CUDA/CANN/MPS for faster processing.

Using GPU, NPU, and MPS for MinerU

Leverage your hardware for accelerated performance:

• GPU (CUDA): Follow the Ubuntu 22.04 or Windows 10/11 guides for GPU setup.
• NPU (Ascend): Utilize the Ascend NPU acceleration tutorial.
• MPS (Apple Silicon): Enable MPS by setting "device-mode": "mps" in magic-pdf.json.

MinerU in Action: Command Line and Python API

Integrate MinerU into your workflow:

• Command Line: Use MinerU via the command line for quick and automated tasks.
• Python API: Utilize the Python API for deeper integration into your projects.

Addressing Known Issues and FAQs

Refer to the Known Issues and FAQs for solutions to common problems. MinerU acknowledges limitations with complex layouts, vertical text, and certain table formats.

License and Acknowledgements

MinerU is open-source. The project uses PyMuPDF, and future iterations aim to utilize a more permissive PDF processing library.

Contribute to MinerU

Your feedback matters! Report issues and contribute to the growing MinerU community. Together, we can make PDF data extraction easier and more efficient.