Changelog

• 2024/09/09: Version 0.8.0 released, supporting fast deployment with Dockerfile, and launching demos on Huggingface and Modelscope.
• 2024/08/30: Version 0.7.1 released, add paddle tablemaster table recognition option
• 2024/08/09: Version 0.7.0b1 released, simplified installation process, added table recognition functionality
• 2024/08/01: Version 0.6.2b1 released, optimized dependency conflict issues and installation documentation
• 2024/07/05: Initial open-source release

MinerU

Project Introduction

MinerU is a tool that converts PDFs into machine-readable formats (e.g., markdown, JSON), allowing for easy extraction into any format. MinerU was born during the pre-training process of InternLM. We focus on solving symbol conversion issues in scientific literature and hope to contribute to technological development in the era of large models. Compared to well-known commercial products, MinerU is still young. If you encounter any issues or if the results are not as expected, please submit an issue on issue and attach the relevant PDF.

https://github.com/user-attachments/assets/4bea02c9-6d54-4cd6-97ed-dff14340982c

Key Features

• Removes elements such as headers, footers, footnotes, and page numbers while maintaining semantic continuity
• Outputs text in a human-readable order from multi-column documents
• Retains the original structure of the document, including titles, paragraphs, and lists
• Extracts images, image captions, tables, and table captions
• Automatically recognizes formulas in the document and converts them to LaTeX
• Automatically recognizes tables in the document and converts them to LaTeX
• Automatically detects and enables OCR for corrupted PDFs
• Supports both CPU and GPU environments
• Supports Windows, Linux, and Mac platforms

Quick Start

If you encounter any installation issues, please first consult the FAQ. If the parsing results are not as expected, refer to the Known Issues. There are three different ways to experience MinerU:

• Online Demo (No Installation Required)
• Quick CPU Demo (Windows, Linux, Mac)
• Linux/Windows + CUDA

⚠️ Pre-installation Notice—Hardware and Software Environment Support

To ensure the stability and reliability of the project, we only optimize and test for specific hardware and software environments during development. This ensures that users deploying and running the project on recommended system configurations will get the best performance with the fewest compatibility issues.

By focusing resources on the mainline environment, our team can more efficiently resolve potential bugs and develop new features.

In non-mainline environments, due to the diversity of hardware and software configurations, as well as third-party dependency compatibility issues, we cannot guarantee 100% project availability. Therefore, for users who wish to use this project in non-recommended environments, we suggest carefully reading the documentation and FAQ first. Most issues already have corresponding solutions in the FAQ. We also encourage community feedback to help us gradually expand support.

Online Demo

Quick CPU Demo

1. Install magic-pdf

conda create -n MinerU python=3.10
conda activate MinerU
pip install -U magic-pdf[full] --extra-index-url https://wheels.myhloli.com

2. Download model weight files

Refer to How to Download Model Files for detailed instructions.

3. Modify the Configuration File for Additional Configuration

After completing the 2. Download model weight files step, the script will automatically generate a magic-pdf.json file in the user directory and configure the default model path. You can find the magic-pdf.json file in your 【user directory】.

The user directory for Windows is "C:\Users\username", for Linux it is "/home/username", and for macOS it is "/Users/username".

You can modify certain configurations in this file to enable or disable features, such as table recognition:

If the following items are not present in the JSON, please manually add the required items and remove the comment content (standard JSON does not support comments).

{
  // other config
  "table-config": {
        "model": "TableMaster", // Another option of this value is 'struct_eqtable'
        "is_table_recog_enable": false, // Table recognition is disabled by default, modify this value to enable it
        "max_time": 400
    }
}

Using GPU

If your device supports CUDA and meets the GPU requirements of the mainline environment, you can use GPU acceleration. Please select the appropriate guide based on your system:

• Ubuntu 22.04 LTS + GPU
• Windows 10/11 + GPU
• Quick Deployment with Docker

  Docker requires a GPU with at least 16GB of VRAM, and all acceleration features are enabled by default.

  Before running this Docker, you can use the following command to check if your device supports CUDA acceleration on Docker.

  docker run --rm --gpus=all nvidia/cuda:12.1.0-base-ubuntu22.04 nvidia-smi
  

  wget https://github.com/opendatalab/MinerU/raw/master/Dockerfile
  docker build -t mineru:latest .
  docker run --rm -it --gpus=all mineru:latest /bin/bash
  magic-pdf --help
  

Usage

Command Line

magic-pdf --help
Usage: magic-pdf [OPTIONS]

Options:
  -v, --version                display the version and exit
  -p, --path PATH              local pdf filepath or directory  [required]
  -o, --output-dir TEXT        output local directory
  -m, --method [ocr|txt|auto]  the method for parsing pdf.
                               ocr: using ocr technique to extract information from pdf,
                               txt: suitable for the text-based pdf only and outperform ocr,
                               auto: automatically choose the best method for parsing pdf
                                  from ocr and txt.
                               without method specified, auto will be used by default.
  --help                       Show this message and exit.


## show version
magic-pdf -v

## command line example
magic-pdf -p {some_pdf} -o {some_output_dir} -m auto

{some_pdf} can be a single PDF file or a directory containing multiple PDFs. The results will be saved in the {some_output_dir} directory. The output file list is as follows:

├── some_pdf.md                          # markdown file
├── images                               # directory for storing images
├── some_pdf_layout.pdf                  # layout diagram
├── some_pdf_middle.json                 # MinerU intermediate processing result
├── some_pdf_model.json                  # model inference result
├── some_pdf_origin.pdf                  # original PDF file
└── some_pdf_spans.pdf                   # smallest granularity bbox position information diagram

For more information about the output files, please refer to the Output File Description.

API

Processing files from local disk

image_writer = DiskReaderWriter(local_image_dir)
image_dir = str(os.path.basename(local_image_dir))
jso_useful_key = {"_pdf_type": "", "model_list": []}
pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer)
pipe.pipe_classify()
pipe.pipe_analyze()
pipe.pipe_parse()
md_content = pipe.pipe_mk_markdown(image_dir, drop_mode="none")

Processing files from object storage

s3pdf_cli = S3ReaderWriter(pdf_ak, pdf_sk, pdf_endpoint)
image_dir = "s3://img_bucket/"
s3image_cli = S3ReaderWriter(img_ak, img_sk, img_endpoint, parent_path=image_dir)
pdf_bytes = s3pdf_cli.read(s3_pdf_path, mode=s3pdf_cli.MODE_BIN)
jso_useful_key = {"_pdf_type": "", "model_list": []}
pipe = UNIPipe(pdf_bytes, jso_useful_key, s3image_cli)
pipe.pipe_classify()
pipe.pipe_analyze()
pipe.pipe_parse()
md_content = pipe.pipe_mk_markdown(image_dir, drop_mode="none")

For detailed implementation, refer to:

• demo.py Simplest Processing Method
• magic_pdf_parse_main.py More Detailed Processing Workflow

Development Guide

TODO

TODO

• Semantic-based reading order
• List recognition within the text
• Code block recognition within the text
• Table of contents recognition
• Table recognition
• Chemical formula recognition
• Geometric shape recognition

Known Issues

• Reading order is segmented based on rules, which can cause disordered sequences in some cases
• Vertical text is not supported
• Lists, code blocks, and table of contents are not yet supported in the layout model
• Comic books, art books, elementary school textbooks, and exercise books are not well-parsed yet
• Enabling OCR may produce better results in PDFs with a high density of formulas
• If you are processing PDFs with a large number of formulas, it is strongly recommended to enable the OCR function. When using PyMuPDF to extract text, overlapping text lines can occur, leading to inaccurate formula insertion positions.

FAQ

FAQ in Chinese

FAQ in English

All Thanks To Our Contributors

License Information

LICENSE.md

This project currently uses PyMuPDF to achieve advanced functionality. However, since it adheres to the AGPL license, it may impose restrictions on certain usage scenarios. In future iterations, we plan to explore and replace it with a more permissive PDF processing library to enhance user-friendliness and flexibility.

Acknowledgments

• PDF-Extract-Kit
• StructEqTable
• PaddleOCR
• PyMuPDF
• fast-langdetect
• pdfminer.six

Citation

@misc{wang2024mineruopensourcesolutionprecise,
      title={MinerU: An Open-Source Solution for Precise Document Content Extraction}, 
      author={Bin Wang and Chao Xu and Xiaomeng Zhao and Linke Ouyang and Fan Wu and Zhiyuan Zhao and Rui Xu and Kaiwen Liu and Yuan Qu and Fukai Shang and Bo Zhang and Liqun Wei and Zhihao Sui and Wei Li and Botian Shi and Yu Qiao and Dahua Lin and Conghui He},
      year={2024},
      eprint={2409.18839},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2409.18839}, 
}

@article{he2024opendatalab,
  title={Opendatalab: Empowering general artificial intelligence with open datasets},
  author={He, Conghui and Li, Wei and Jin, Zhenjiang and Xu, Chao and Wang, Bin and Lin, Dahua},
  journal={arXiv preprint arXiv:2407.13773},
  year={2024}
}

Star History

Magic-doc

Magic-Doc Fast speed ppt/pptx/doc/docx/pdf extraction tool

Magic-html

Magic-HTML Mixed web page extraction tool

Links

• LabelU (A Lightweight Multi-modal Data Annotation Tool)
• LabelLLM (An Open-source LLM Dialogue Annotation Platform)
• PDF-Extract-Kit (A Comprehensive Toolkit for High-Quality PDF Content Extraction)