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align=\"center\" xmlns=\"http://www.w3.org/1999/html\"\u003e\n\u003c!-- logo --\u003e\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/docs/images/MinerU-logo.png\" width=\"300px\" style=\"vertical-align:middle;\"\u003e\n\u003c/p\u003e\n\n\u003c!-- icon --\u003e\n\n[![stars](https://img.shields.io/github/stars/opendatalab/MinerU.svg)](https://github.com/opendatalab/MinerU)\n[![forks](https://img.shields.io/github/forks/opendatalab/MinerU.svg)](https://github.com/opendatalab/MinerU)\n[![open issues](https://img.shields.io/github/issues-raw/opendatalab/MinerU)](https://github.com/opendatalab/MinerU/issues)\n[![issue resolution](https://img.shields.io/github/issues-closed-raw/opendatalab/MinerU)](https://github.com/opendatalab/MinerU/issues)\n[![PyPI version](https://img.shields.io/pypi/v/mineru)](https://pypi.org/project/mineru/)\n[![PyPI - Python 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co/spaces/opendatalab/MinerU)\n[![ModelScope](https://img.shields.io/badge/Demo_on_ModelScope-purple?logo=data:image/svg+xml;base64,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\u0026labelColor=white)](https://www.modelscope.cn/studios/OpenDataLab/MinerU)\n[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/myhloli/a3cb16570ab3cfeadf9d8f0ac91b4fca/mineru_demo.ipynb)\n[![arXiv](https://img.shields.io/badge/MinerU-Technical%20Report-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2409.18839)\n[![arXiv](https://img.shields.io/badge/MinerU2.5-Technical%20Report-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2509.22186)\n[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/opendatalab/MinerU)\n\n\n\u003ca href=\"https://trendshift.io/repositories/11174\" target=\"_blank\"\u003e\u003cimg src=\"https://trendshift.io/api/badge/repositories/11174\" alt=\"opendatalab%2FMinerU | Trendshift\" style=\"width: 250px; height: 55px;\" width=\"250\" height=\"55\"/\u003e\u003c/a\u003e\n\n\u003c!-- language --\u003e\n\n[English](README.md) | [简体中文](README_zh-CN.md)\n\n\u003c!-- hot link --\u003e\n\n\u003cp align=\"center\"\u003e\n🚀\u003ca href=\"https://mineru.net/?source=github\"\u003eAccess MinerU Now→✅ Zero-Install Web Version ✅ Full-Featured Desktop Client ✅ Instant API Access; Skip deployment headaches – get all product formats in one click. Developers, dive in!\u003c/a\u003e\n\u003c/p\u003e\n\n\u003c!-- join us --\u003e\n\n\u003cp align=\"center\"\u003e\n    👋 join us on \u003ca href=\"https://discord.gg/Tdedn9GTXq\" target=\"_blank\"\u003eDiscord\u003c/a\u003e and \u003ca href=\"https://mineru.net/community-portal/?aliasId=3c430f94\" target=\"_blank\"\u003eWeChat\u003c/a\u003e\n\u003c/p\u003e\n\n\u003c/div\u003e\n\n# Changelog\n\n- 2026/01/06 2.7.1 Release\n  - fix bug: #4300\n  - Updated pdfminer.six dependency version to resolve [CVE-2025-64512](https://github.com/advisories/GHSA-wf5f-4jwr-ppcp)\n  - Support automatic correction of input image exif orientation to improve OCR recognition accuracy  #4283\n\n- 2025/12/30 2.7.0 Release\n  - Simplified installation process. No need to separately install `vlm` acceleration engine dependencies. Using `uv pip install mineru[all]` during installation will install all optional backend dependencies.\n  - Added new `hybrid` backend, which combines the advantages of `pipeline` and `vlm` backends. Built on vlm, it integrates some capabilities of pipeline, adding extra extensibility on top of high accuracy:\n    - Directly extracts text from text PDFs, natively supports multi-language recognition in text PDF scenarios, and greatly reduces parsing hallucinations;\n    - Supports text recognition in 109 languages for scanned PDF scenarios by specifying OCR language;\n    - Independent inline formula recognition switch, which can be disabled separately when inline formula recognition is not needed, improving the visual effect of parsing results.\n  - Simplified engine selection logic for `vlm/hybrid` backends. Users only need to specify the backend as `*-auto-engine`, and the system will automatically select the appropriate engine for inference acceleration based on the current environment, improving usability.\n  - Switched default parsing backend from `pipeline` to `hybrid-auto-engine`, improving out-of-the-box result consistency for new users and avoiding cognitive differences in parsing results.\n  - Added i18n support to gradio application, supporting switching between Chinese and English languages.\n  \n\u003e 📝 View the complete [Changelog](https://opendatalab.github.io/MinerU/reference/changelog/) for more historical version information\n\n# MinerU\n\n## Project Introduction\n\nMinerU is a tool that converts PDFs into machine-readable formats (e.g., markdown, JSON), allowing for easy extraction into any format.\nMinerU was born during the pre-training process of [InternLM](https://github.com/InternLM/InternLM). We focus on solving symbol conversion issues in scientific literature and hope to contribute to technological development in the era of large models.\nCompared 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](https://github.com/opendatalab/MinerU/issues) and **attach the relevant PDF**.\n\nhttps://github.com/user-attachments/assets/4bea02c9-6d54-4cd6-97ed-dff14340982c\n\n## Key Features\n\n- Remove headers, footers, footnotes, page numbers, etc., to ensure semantic coherence.\n- Output text in human-readable order, suitable for single-column, multi-column, and complex layouts.\n- Preserve the structure of the original document, including headings, paragraphs, lists, etc.\n- Extract images, image descriptions, tables, table titles, and footnotes.\n- Automatically recognize and convert formulas in the document to LaTeX format.\n- Automatically recognize and convert tables in the document to HTML format.\n- Automatically detect scanned PDFs and garbled PDFs and enable OCR functionality.\n- OCR supports detection and recognition of 109 languages.\n- Supports multiple output formats, such as multimodal and NLP Markdown, JSON sorted by reading order, and rich intermediate formats.\n- Supports various visualization results, including layout visualization and span visualization, for efficient confirmation of output quality.\n- Supports running in a pure CPU environment, and also supports GPU(CUDA)/NPU(CANN)/MPS acceleration\n- Compatible with Windows, Linux, and Mac platforms.\n\n# Quick Start\n\nIf you encounter any installation issues, please first consult the \u003ca href=\"#faq\"\u003eFAQ\u003c/a\u003e. \u003c/br\u003e\nIf the parsing results are not as expected, refer to the \u003ca href=\"#known-issues\"\u003eKnown Issues\u003c/a\u003e. \u003c/br\u003e\n\n## Online Experience\n\n### Official online web application\nThe official online version has the same functionality as the client, with a beautiful interface and rich features, requires login to use  \n \n- [![OpenDataLab](https://img.shields.io/badge/webapp_on_mineru.net-blue?logo=data:image/svg+xml;base64,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\u0026labelColor=white)](https://mineru.net/OpenSourceTools/Extractor?source=github)\n\n### Gradio-based online demo\nA WebUI developed based on Gradio, with a simple interface and only core parsing functionality, no login required  \n\n- [![ModelScope](https://img.shields.io/badge/Demo_on_ModelScope-purple?logo=data:image/svg+xml;base64,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\u0026labelColor=white)](https://www.modelscope.cn/studios/OpenDataLab/MinerU)\n- [![HuggingFace](https://img.shields.io/badge/Demo_on_HuggingFace-yellow.svg?logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAF8AAABYCAMAAACkl9t/AAAAk1BMVEVHcEz/nQv/nQv/nQr/nQv/nQr/nQv/nQv/nQr/wRf/txT/pg7/yRr/rBD/zRz/ngv/oAz/zhz/nwv/txT/ngv/0B3+zBz/nQv/0h7/wxn/vRb/thXkuiT/rxH/pxD/ogzcqyf/nQvTlSz/czCxky7/SjifdjT/Mj3+Mj3wMj15aTnDNz+DSD9RTUBsP0FRO0Q6O0WyIxEIAAAAGHRSTlMADB8zSWF3krDDw8TJ1NbX5efv8ff9/fxKDJ9uAAAGKklEQVR42u2Z63qjOAyGC4RwCOfB2JAGqrSb2WnTw/1f3UaWcSGYNKTdf/P+mOkTrE+yJBulvfvLT2A5ruenaVHyIks33npl/6C4s/ZLAM45SOi/1FtZPyFur1OYofBX3w7d54Bxm+E8db+nDr12ttmESZ4zludJEG5S7TO72YPlKZFyE+YCYUJTBZsMiNS5Sd7NlDmKM2Eg2JQg8awbglfqgbhArjxkS7dgp2RH6hc9AMLdZYUtZN5DJr4molC8BfKrEkPKEnEVjLbgW1fLy77ZVOJagoIcLIl+IxaQZGjiX597HopF5CkaXVMDO9Pyix3AFV3kw4lQLCbHuMovz8FallbcQIJ5Ta0vks9RnolbCK84BtjKRS5uA43hYoZcOBGIG2Epbv6CvFVQ8m8loh66WNySsnN7htL58LNp+NXT8/PhXiBXPMjLSxtwp8W9f/1AngRierBkA+kk/IpUSOeKByzn8y3kAAAfh//0oXgV4roHm/kz4E2z//zRc3/lgwBzbM2mJxQEa5pqgX7d1L0htrhx7LKxOZlKbwcAWyEOWqYSI8YPtgDQVjpB5nvaHaSnBaQSD6hweDi8PosxD6/PT09YY3xQA7LTCTKfYX+QHpA0GCcqmEHvr/cyfKQTEuwgbs2kPxJEB0iNjfJcCTPyocx+A0griHSmADiC91oNGVwJ69RudYe65vJmoqfpul0lrqXadW0jFKH5BKwAeCq+Den7s+3zfRJzA61/Uj/9H/VzLKTx9jFPPdXeeP+L7WEvDLAKAIoF8bPTKT0+TM7W8ePj3Rz/Yn3kOAp2f1Kf0Weony7pn/cPydvhQYV+eFOfmOu7VB/ViPe34/EN3RFHY/yRuT8ddCtMPH/McBAT5s+vRde/gf2c/sPsjLK+m5IBQF5tO+h2tTlBGnP6693JdsvofjOPnnEHkh2TnV/X1fBl9S5zrwuwF8NFrAVJVwCAPTe8gaJlomqlp0pv4Pjn98tJ/t/fL++6unpR1YGC2n/KCoa0tTLoKiEeUPDl94nj+5/Tv3/eT5vBQ60X1S0oZr+IWRR8Ldhu7AlLjPISlJcO9vrFotky9SpzDequlwEir5beYAc0R7D9KS1DXva0jhYRDXoExPdc6yw5GShkZXe9QdO/uOvHofxjrV/TNS6iMJS+4TcSTgk9n5agJdBQbB//IfF/HpvPt3Tbi7b6I6K0R72p6ajryEJrENW2bbeVUGjfgoals4L443c7BEE4mJO2SpbRngxQrAKRudRzGQ8jVOL2qDVjjI8K1gc3TIJ5KiFZ1q+gdsARPB4NQS4AjwVSt72DSoXNyOWUrU5mQ9nRYyjp89Xo7oRI6Bga9QNT1mQ/ptaJq5T/7WcgAZywR/XlPGAUDdet3LE+qS0TI+g+aJU8MIqjo0Kx8Ly+maxLjJmjQ18rA0YCkxLQbUZP1WqdmyQGJLUm7VnQFqodmXSqmRrdVpqdzk5LvmvgtEcW8PMGdaS23EOWyDVbACZzUJPaqMbjDxpA3Qrgl0AikimGDbqmyT8P8NOYiqrldF8rX+YN7TopX4UoHuSCYY7cgX4gHwclQKl1zhx0THf+tCAUValzjI7Wg9EhptrkIcfIJjA94evOn8B2eHaVzvBrnl2ig0So6hvPaz0IGcOvTHvUIlE2+prqAxLSQxZlU2stql1NqCCLdIiIN/i1DBEHUoElM9dBravbiAnKqgpi4IBkw+utSPIoBijDXJipSVV7MpOEJUAc5Qmm3BnUN+w3hteEieYKfRZSIUcXKMVf0u5wD4EwsUNVvZOtUT7A2GkffHjByWpHqvRBYrTV72a6j8zZ6W0DTE86Hn04bmyWX3Ri9WH7ZU6Q7h+ZHo0nHUAcsQvVhXRDZHChwiyi/hnPuOsSEF6Exk3o6Y9DT1eZ+6cASXk2Y9k+6EOQMDGm6WBK10wOQJCBwren86cPPWUcRAnTVjGcU1LBgs9FURiX/e6479yZcLwCBmTxiawEwrOcleuu12t3tbLv/N4RLYIBhYexm7Fcn4OJcn0+zc+s8/VfPeddZHAGN6TT8eGczHdR/Gts1/MzDkThr23zqrVfAMFT33Nx1RJsx1k5zuWILLnG/vsH+Fv5D4NTVcp1Gzo8AAAAAElFTkSuQmCC\u0026labelColor=white)](https://huggingface.co/spaces/opendatalab/MinerU)\n\n## Local Deployment\n\n\n\u003e [!WARNING]\n\u003e **Pre-installation Notice—Hardware and Software Environment Support**\n\u003e\n\u003e 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.\n\u003e\n\u003e By focusing resources on the mainline environment, our team can more efficiently resolve potential bugs and develop new features.\n\u003e\n\u003e 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.\n\n\u003ctable\u003e\n  \u003cthead\u003e\n    \u003ctr\u003e\n      \u003cth rowspan=\"2\"\u003eParsing Backend\u003c/th\u003e\n      \u003cth rowspan=\"2\"\u003epipeline\u003c/th\u003e\n      \u003cth colspan=\"2\"\u003e*-auto-engine\u003c/th\u003e\n      \u003cth colspan=\"2\"\u003e*-http-client\u003c/th\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003ehybrid\u003c/th\u003e\n      \u003cth\u003evlm\u003c/th\u003e\n      \u003cth\u003ehybrid\u003c/th\u003e\n      \u003cth\u003evlm\u003c/th\u003e\n    \u003c/tr\u003e\n  \u003c/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n      \u003cth\u003eBackend Features\u003c/th\u003e\n      \u003ctd \u003eGood Compatibility\u003c/td\u003e\n      \u003ctd colspan=\"2\"\u003eHigh Hardware Requirements\u003c/td\u003e\n      \u003ctd colspan=\"2\"\u003eFor OpenAI Compatible Servers\u003csup\u003e2\u003c/sup\u003e\u003c/td\u003e\n    \u003c/tr\u003e \n    \u003ctr\u003e\n      \u003cth\u003eAccuracy\u003csup\u003e1\u003c/sup\u003e\u003c/th\u003e\n      \u003ctd style=\"text-align:center;\"\u003e82+\u003c/td\u003e\n      \u003ctd colspan=\"4\" style=\"text-align:center;\"\u003e90+\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003eOperating System\u003c/th\u003e\n      \u003ctd colspan=\"5\" style=\"text-align:center;\"\u003eLinux\u003csup\u003e3\u003c/sup\u003e / Windows\u003csup\u003e4\u003c/sup\u003e / macOS\u003csup\u003e5\u003c/sup\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003ePure CPU Support\u003c/th\u003e\n      \u003ctd style=\"text-align:center;\"\u003e✅\u003c/td\u003e\n      \u003ctd colspan=\"2\" style=\"text-align:center;\"\u003e❌\u003c/td\u003e\n      \u003ctd colspan=\"2\" style=\"text-align:center;\"\u003e✅\u003c/td\u003e\n    \u003c/tr\u003e\n        \u003ctr\u003e\n      \u003cth\u003eGPU Acceleration\u003c/th\u003e\n      \u003ctd colspan=\"4\" style=\"text-align:center;\"\u003eVolta and later architecture GPUs or Apple Silicon\u003c/td\u003e\n      \u003ctd rowspan=\"2\"\u003eNot Required\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003eMin VRAM\u003c/th\u003e\n      \u003ctd style=\"text-align:center;\"\u003e6GB\u003c/td\u003e\n      \u003ctd style=\"text-align:center;\"\u003e10GB\u003c/td\u003e\n      \u003ctd style=\"text-align:center;\"\u003e8GB\u003c/td\u003e\n      \u003ctd style=\"text-align:center;\"\u003e3GB\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003eRAM\u003c/th\u003e\n      \u003ctd colspan=\"3\" style=\"text-align:center;\"\u003eMin 16GB, Recommended 32GB or more\u003c/td\u003e\n      \u003ctd colspan=\"2\" style=\"text-align:center;\"\u003eMin 8GB\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003eDisk Space\u003c/th\u003e\n      \u003ctd colspan=\"3\" style=\"text-align:center;\"\u003eMin 20GB, SSD Recommended\u003c/td\u003e\n      \u003ctd colspan=\"2\" style=\"text-align:center;\"\u003eMin 2GB\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003ePython Version\u003c/th\u003e\n      \u003ctd colspan=\"5\" style=\"text-align:center;\"\u003e3.10-3.13\u003c/td\u003e\n    \u003c/tr\u003e\n  \u003c/tbody\u003e\n\u003c/table\u003e\n\n\u003csup\u003e1\u003c/sup\u003e Accuracy metrics are the End-to-End Evaluation Overall scores from OmniDocBench (v1.5), based on the latest version of `MinerU`.  \n\u003csup\u003e2\u003c/sup\u003e Servers compatible with OpenAI API, such as local model servers or remote model services deployed via inference frameworks like `vLLM`/`SGLang`/`LMDeploy`.  \n\u003csup\u003e3\u003c/sup\u003e Linux only supports distributions from 2019 and later.  \n\u003csup\u003e4\u003c/sup\u003e Since the key dependency `ray` does not support Python 3.13 on Windows, only versions 3.10~3.12 are supported.  \n\u003csup\u003e5\u003c/sup\u003e macOS requires version 14.0 or later.\n\n\n### Install MinerU\n\n#### Install MinerU using pip or uv\n```bash\npip install --upgrade pip\npip install uv\nuv pip install -U \"mineru[all]\"\n```\n\n#### Install MinerU from source code\n```bash\ngit clone https://github.com/opendatalab/MinerU.git\ncd MinerU\nuv pip install -e .[all]\n```\n\n\u003e [!TIP]\n\u003e `mineru[all]` includes all core features, compatible with Windows / Linux / macOS systems, suitable for most users.\n\u003e If you need to specify the inference framework for the VLM model, or only intend to install a lightweight client on an edge device, please refer to the documentation [Extension Modules Installation Guide](https://opendatalab.github.io/MinerU/quick_start/extension_modules/).\n\n---\n \n#### Deploy MinerU using Docker\nMinerU provides a convenient Docker deployment method, which helps quickly set up the environment and solve some tricky environment compatibility issues.\nYou can get the [Docker Deployment Instructions](https://opendatalab.github.io/MinerU/quick_start/docker_deployment/) in the documentation.\n\n---\n\n### Using MinerU\n\n\nIf your device meets the GPU acceleration requirements in the table above, you can use a simple command line for document parsing:\n```bash\nmineru -p \u003cinput_path\u003e -o \u003coutput_path\u003e\n```\nIf your device does not meet the GPU acceleration requirements, you can specify the backend as `pipeline` to run in a pure CPU environment:\n```bash\nmineru -p \u003cinput_path\u003e -o \u003coutput_path\u003e -b pipeline\n```\n\nYou can use MinerU for PDF parsing through various methods such as command line, API, and WebUI. For detailed instructions, please refer to the [Usage Guide](https://opendatalab.github.io/MinerU/usage/).\n\n# TODO\n\n- [x] Reading order based on the model  \n- [x] Recognition of `index` and `list` in the main text  \n- [x] Table recognition\n- [x] Heading Classification\n- [x] Handwritten Text Recognition  \n- [x] Vertical Text Recognition  \n- [x] Latin Accent Mark Recognition\n- [x] Code block recognition in the main text\n- [x] [Chemical formula recognition](docs/chemical_knowledge_introduction/introduction.pdf)(mineru.net)\n- [ ] Geometric shape recognition\n\n# Known Issues\n\n- 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.\n- Limited support for vertical text.\n- Tables of contents and lists are recognized through rules, and some uncommon list formats may not be recognized.\n- Code blocks are not yet supported in the layout model.\n- Comic books, art albums, primary school textbooks, and exercises cannot be parsed well.\n- Table recognition may result in row/column recognition errors in complex tables.\n- 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).\n- Some formulas may not render correctly in Markdown.\n\n# FAQ\n\n- If you encounter any issues during usage, you can first check the [FAQ](https://opendatalab.github.io/MinerU/faq/) for solutions.  \n- If your issue remains unresolved, you may also use [DeepWiki](https://deepwiki.com/opendatalab/MinerU) to interact with an AI assistant, which can address most common problems.  \n- If you still cannot resolve the issue, you are welcome to join our community via [Discord](https://discord.gg/Tdedn9GTXq) or [WeChat](https://mineru.net/community-portal/?aliasId=3c430f94) to discuss with other users and developers.\n\n# All Thanks To Our Contributors\n\n\u003ca href=\"https://github.com/opendatalab/MinerU/graphs/contributors\"\u003e\n  \u003cimg src=\"https://contrib.rocks/image?repo=opendatalab/MinerU\" /\u003e\n\u003c/a\u003e\n\n# License Information\n\n[LICENSE.md](LICENSE.md)\n\nCurrently, some models in this project are trained based on YOLO. However, since YOLO follows the AGPL license, it may impose restrictions on certain use cases. In future iterations, we plan to explore and replace these with models under more permissive licenses to enhance user-friendliness and flexibility.\n\n# Acknowledgments\n\n- [PDF-Extract-Kit](https://github.com/opendatalab/PDF-Extract-Kit)\n- [DocLayout-YOLO](https://github.com/opendatalab/DocLayout-YOLO)\n- [UniMERNet](https://github.com/opendatalab/UniMERNet)\n- [RapidTable](https://github.com/RapidAI/RapidTable)\n- [TableStructureRec](https://github.com/RapidAI/TableStructureRec)\n- [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)\n- [PaddleOCR2Pytorch](https://github.com/frotms/PaddleOCR2Pytorch)\n- [layoutreader](https://github.com/ppaanngggg/layoutreader)\n- [xy-cut](https://github.com/Sanster/xy-cut)\n- [fast-langdetect](https://github.com/LlmKira/fast-langdetect)\n- [pypdfium2](https://github.com/pypdfium2-team/pypdfium2)\n- [pdftext](https://github.com/datalab-to/pdftext)\n- [pdfminer.six](https://github.com/pdfminer/pdfminer.six)\n- [pypdf](https://github.com/py-pdf/pypdf)\n- [magika](https://github.com/google/magika)\n- [vLLM](https://github.com/vllm-project/vllm)\n- [LMDeploy](https://github.com/InternLM/lmdeploy)\n\n# Citation\n\n```bibtex\n@misc{niu2025mineru25decoupledvisionlanguagemodel,\n      title={MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing}, \n      author={Junbo Niu and Zheng Liu and Zhuangcheng Gu and Bin Wang and Linke Ouyang and Zhiyuan Zhao and Tao Chu and Tianyao He and Fan Wu and Qintong Zhang and Zhenjiang Jin and Guang Liang and Rui Zhang and Wenzheng Zhang and Yuan Qu and Zhifei Ren and Yuefeng Sun and Yuanhong Zheng and Dongsheng Ma and Zirui Tang and Boyu Niu and Ziyang Miao and Hejun Dong and Siyi Qian and Junyuan Zhang and Jingzhou Chen and Fangdong Wang and Xiaomeng Zhao and Liqun Wei and Wei Li and Shasha Wang and Ruiliang Xu and Yuanyuan Cao and Lu Chen and Qianqian Wu and Huaiyu Gu and Lindong Lu and Keming Wang and Dechen Lin and Guanlin Shen and Xuanhe Zhou and Linfeng Zhang and Yuhang Zang and Xiaoyi Dong and Jiaqi Wang and Bo Zhang and Lei Bai and Pei Chu and Weijia Li and Jiang Wu and Lijun Wu and Zhenxiang Li and Guangyu Wang and Zhongying Tu and Chao Xu and Kai Chen and Yu Qiao and Bowen Zhou and Dahua Lin and Wentao Zhang and Conghui He},\n      year={2025},\n      eprint={2509.22186},\n      archivePrefix={arXiv},\n      primaryClass={cs.CV},\n      url={https://arxiv.org/abs/2509.22186}, \n}\n\n@misc{wang2024mineruopensourcesolutionprecise,\n      title={MinerU: An Open-Source Solution for Precise Document Content Extraction}, \n      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},\n      year={2024},\n      eprint={2409.18839},\n      archivePrefix={arXiv},\n      primaryClass={cs.CV},\n      url={https://arxiv.org/abs/2409.18839}, \n}\n\n@article{he2024opendatalab,\n  title={Opendatalab: Empowering general artificial intelligence with open datasets},\n  author={He, Conghui and Li, Wei and Jin, Zhenjiang and Xu, Chao and Wang, Bin and Lin, Dahua},\n  journal={arXiv preprint arXiv:2407.13773},\n  year={2024}\n}\n```\n\n# Star History\n\n\u003ca\u003e\n \u003cpicture\u003e\n   \u003csource media=\"(prefers-color-scheme: dark)\" srcset=\"https://api.star-history.com/svg?repos=opendatalab/MinerU\u0026type=Date\u0026theme=dark\" /\u003e\n   \u003csource media=\"(prefers-color-scheme: light)\" srcset=\"https://api.star-history.com/svg?repos=opendatalab/MinerU\u0026type=Date\" /\u003e\n   \u003cimg alt=\"Star History Chart\" src=\"https://api.star-history.com/svg?repos=opendatalab/MinerU\u0026type=Date\" /\u003e\n \u003c/picture\u003e\n\u003c/a\u003e\n\n\n# Links\n- [Easy Data Preparation with latest LLMs-based Operators and Pipelines](https://github.com/OpenDCAI/DataFlow)\n- [Vis3 (OSS browser based on s3)](https://github.com/opendatalab/Vis3)\n- [LabelU (A Lightweight Multi-modal Data Annotation Tool)](https://github.com/opendatalab/labelU)\n- [LabelLLM (An Open-source LLM Dialogue Annotation Platform)](https://github.com/opendatalab/LabelLLM)\n- [PDF-Extract-Kit (A Comprehensive Toolkit for High-Quality PDF Content Extraction)](https://github.com/opendatalab/PDF-Extract-Kit)\n- [OmniDocBench (A Comprehensive Benchmark for Document Parsing and Evaluation)](https://github.com/opendatalab/OmniDocBench)\n- [Magic-HTML (Mixed web page extraction tool)](https://github.com/opendatalab/magic-html)\n- [Magic-Doc (Fast speed ppt/pptx/doc/docx/pdf extraction tool)](https://github.com/InternLM/magic-doc) \n- [Dingo: A Comprehensive AI Data Quality Evaluation Tool](https://github.com/MigoXLab/dingo)\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fopendatalab%2Fmineru","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fopendatalab%2Fmineru","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fopendatalab%2Fmineru/lists"}