Changelog

• 2025/03/03 1.2.1 released, fixed several bugs:
  • Fixed the impact on punctuation marks during full-width to half-width conversion of letters and numbers
  • Fixed caption matching inaccuracies in certain scenarios
  • Fixed formula span loss issues in certain scenarios
• 2025/02/24 1.2.0 released. This version includes several fixes and improvements to enhance parsing efficiency and accuracy:
  • Performance Optimization
    • Increased classification speed for PDF documents in auto mode.
  • Parsing Optimization
    • Improved parsing logic for documents containing watermarks, significantly enhancing the parsing results for such documents.
    • Enhanced the matching logic for multiple images/tables and captions within a single page, improving the accuracy of image-text matching in complex layouts.
  • Bug Fixes
    • Fixed an issue where image/table spans were incorrectly filled into text blocks under certain conditions.
    • Resolved an issue where title blocks were empty in some cases.
• 2025/01/22 1.1.0 released. In this version we have focused on improving parsing accuracy and efficiency:
  • Model capability upgrade (requires re-executing the model download process to obtain incremental updates of model files)
    • The layout recognition model has been upgraded to the latest doclayout_yolo(2501) model, improving layout recognition accuracy.
    • The formula parsing model has been upgraded to the latest unimernet(2501) model, improving formula recognition accuracy.
  • Performance optimization
    • On devices that meet certain configuration requirements (16GB+ VRAM), by optimizing resource usage and restructuring the processing pipeline, overall parsing speed has been increased by more than 50%.
  • Parsing effect optimization
    • Added a new heading classification feature (testing version, enabled by default) to the online demo(mineru.net/huggingface/modelscope), which supports hierarchical classification of headings, thereby enhancing document structuring.
• 2025/01/10 1.0.1 released. This is our first official release, where we have introduced a completely new API interface and enhanced compatibility through extensive refactoring, as well as a brand new automatic language identification feature:
  • New API Interface
    • For the data-side API, we have introduced the Dataset class, designed to provide a robust and flexible data processing framework. This framework currently supports a variety of document formats, including images (.jpg and .png), PDFs, Word documents (.doc and .docx), and PowerPoint presentations (.ppt and .pptx). It ensures effective support for data processing tasks ranging from simple to complex.
    • For the user-side API, we have meticulously designed the MinerU processing workflow as a series of composable Stages. Each Stage represents a specific processing step, allowing users to define new Stages according to their needs and creatively combine these stages to customize their data processing workflows.
  • Enhanced Compatibility
    • By optimizing the dependency environment and configuration items, we ensure stable and efficient operation on ARM architecture Linux systems.
    • We have deeply integrated with Huawei Ascend NPU acceleration, providing autonomous and controllable high-performance computing capabilities. This supports the localization and development of AI application platforms in China. Ascend NPU Acceleration
  • Automatic Language Identification
    • By introducing a new language recognition model, setting the lang configuration to auto during document parsing will automatically select the appropriate OCR language model, improving the accuracy of scanned document parsing.
• 2024/11/22 0.10.0 released. Introducing hybrid OCR text extraction capabilities,
  • Significantly improved parsing performance in complex text distribution scenarios such as dense formulas, irregular span regions, and text represented by images.
  • Combines the dual advantages of accurate content extraction and faster speed in text mode, and more precise span/line region recognition in OCR mode.
• 2024/11/15 0.9.3 released. Integrated RapidTable for table recognition, improving single-table parsing speed by more than 10 times, with higher accuracy and lower GPU memory usage.
• 2024/11/06 0.9.2 released. Integrated the StructTable-InternVL2-1B model for table recognition functionality.
• 2024/10/31 0.9.0 released. This is a major new version with extensive code refactoring, addressing numerous issues, improving performance, reducing hardware requirements, and enhancing usability:
  • Refactored the sorting module code to use layoutreader for reading order sorting, ensuring high accuracy in various layouts.
  • Refactored the paragraph concatenation module to achieve good results in cross-column, cross-page, cross-figure, and cross-table scenarios.
  • Refactored the list and table of contents recognition functions, significantly improving the accuracy of list blocks and table of contents blocks, as well as the parsing of corresponding text paragraphs.
  • Refactored the matching logic for figures, tables, and descriptive text, greatly enhancing the accuracy of matching captions and footnotes to figures and tables, and reducing the loss rate of descriptive text to near zero.
  • Added multi-language support for OCR, supporting detection and recognition of 84 languages.For the list of supported languages, see OCR Language Support List.
  • Added memory recycling logic and other memory optimization measures, significantly reducing memory usage. The memory requirement for enabling all acceleration features except table acceleration (layout/formula/OCR) has been reduced from 16GB to 8GB, and the memory requirement for enabling all acceleration features has been reduced from 24GB to 10GB.
  • Optimized configuration file feature switches, adding an independent formula detection switch to significantly improve speed and parsing results when formula detection is not needed.
  • Integrated PDF-Extract-Kit 1.0:
    • Added the self-developed doclayout_yolo model, which speeds up processing by more than 10 times compared to the original solution while maintaining similar parsing effects, and can be freely switched with layoutlmv3 via the configuration file.
    • Upgraded formula parsing to unimernet 0.2.1, improving formula parsing accuracy while significantly reducing memory usage.
    • Due to the repository change for PDF-Extract-Kit 1.0, you need to re-download the model. Please refer to How to Download Models for detailed steps.
• 2024/09/27 Version 0.8.1 released, Fixed some bugs, and providing a localized deployment version of the online demo and the front-end interface.
• 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

• Remove headers, footers, footnotes, page numbers, etc., to ensure semantic coherence.
• Output text in human-readable order, suitable for single-column, multi-column, and complex layouts.
• Preserve the structure of the original document, including headings, paragraphs, lists, etc.
• Extract images, image descriptions, tables, table titles, and footnotes.
• Automatically recognize and convert formulas in the document to LaTeX format.
• Automatically recognize and convert tables in the document to HTML format.
• Automatically detect scanned PDFs and garbled PDFs and enable OCR functionality.
• OCR supports detection and recognition of 84 languages.
• Supports multiple output formats, such as multimodal and NLP Markdown, JSON sorted by reading order, and rich intermediate formats.
• Supports various visualization results, including layout visualization and span visualization, for efficient confirmation of output quality.
• Supports running in a pure CPU environment, and also supports GPU(CUDA)/NPU(CANN)/MPS acceleration
• Compatible with 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)
• Accelerate inference by using CUDA/CANN/MPS
  • Linux/Windows + CUDA
  • Linux + CANN
  • MacOS + MPS

[!WARNING] 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

Synced with dev branch updates:

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】.

[!TIP] 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:

[!NOTE] 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
    "layout-config": {
        "model": "doclayout_yolo" // Please change to "layoutlmv3" when using layoutlmv3.
    },
    "formula-config": {
        "mfd_model": "yolo_v8_mfd",
        "mfr_model": "unimernet_small",
        "enable": true  // The formula recognition feature is enabled by default. If you need to disable it, please change the value here to "false".
    },
    "table-config": {
        "model": "rapid_table",  // Default to using "rapid_table", can be switched to "tablemaster" or "struct_eqtable".
        "sub_model": "slanet_plus",  // When the model is "rapid_table", you can choose a sub_model. The options are "slanet_plus" and "unitable"
        "enable": true, // The table recognition feature is enabled by default. If you need to disable it, please change the value here to "false".
        "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

  [!IMPORTANT] Docker requires a GPU with at least 8GB 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/docker/global/Dockerfile -O Dockerfile
docker build -t mineru:latest .
docker run -it --name mineru --gpus=all mineru:latest /bin/bash -c "echo 'source /opt/mineru_venv/bin/activate' >> ~/.bashrc && exec bash"
magic-pdf --help

Using NPU

If your device has NPU acceleration hardware, you can follow the tutorial below to use NPU acceleration:

Ascend NPU Acceleration

Using MPS

If your device uses Apple silicon chips, you can enable MPS acceleration for certain supported tasks (such as layout detection and formula detection).

You can enable MPS acceleration by setting the device-mode parameter to mps in the magic-pdf.json configuration file.

{
    // other config
    "device-mode": "mps"
}

[!TIP] Since the formula recognition task cannot utilize MPS acceleration, you can disable the formula recognition feature in tasks where it is not needed to achieve optimal performance.

You can disable the formula recognition feature by setting the enable parameter in the formula-config section to false.

Usage

Command Line

Using MinerU via Command Line

[!TIP] For more information about the output files, please refer to the Output File Description.

API

Using MinerU via Python API

Deploy Derived Projects

Derived projects include secondary development projects based on MinerU by project developers and community developers,
such as application interfaces based on Gradio, RAG based on llama, web demos similar to the official website, lightweight multi-GPU load balancing client/server ends, etc. These projects may offer more features and a better user experience.
For specific deployment methods, please refer to the Derived Project README

Development Guide

TODO

TODO

• Reading order based on the model
• Recognition of index and list in the main text
• Table recognition
• Heading Classification
• Code block recognition in the main text
• Chemical formula recognition
• Geometric shape recognition

Known Issues

• Reading order is determined by the model based on the spatial distribution of readable content, and may be out of order in some areas under extremely complex layouts.
• Vertical text is not supported.
• Tables of contents and lists are recognized through rules, and some uncommon list formats may not be recognized.
• Code blocks are not yet supported in the layout model.
• Comic books, art albums, primary school textbooks, and exercises cannot be parsed well.
• Table recognition may result in row/column recognition errors in complex tables.
• OCR recognition may produce inaccurate characters in PDFs of lesser-known languages (e.g., diacritical marks in Latin script, easily confused characters in Arabic script).
• Some formulas may not render correctly in Markdown.

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
• DocLayout-YOLO
• StructEqTable
• RapidTable
• PaddleOCR
• PyMuPDF
• layoutreader
• 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)