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# 更新记录 - 2026/02/06 2.7.6 发布 - 新增国产算力平台昆仑芯、太初元碁的适配支持，目前已由官方和厂商适配并支持的国产算力平台包括: - [昇腾 Ascend](https://opendatalab.github.io/MinerU/zh/usage/acceleration_cards/Ascend) - [平头哥 T-Head](https://opendatalab.github.io/MinerU/zh/usage/acceleration_cards/THead) - [沐曦 METAX](https://opendatalab.github.io/MinerU/zh/usage/acceleration_cards/METAX) - [海光 Hygon](https://opendatalab.github.io/MinerU/zh/usage/acceleration_cards/Hygon/) - [燧原 Enflame](https://opendatalab.github.io/MinerU/zh/usage/acceleration_cards/Enflame/) - [摩尔线程 MooreThreads](https://opendatalab.github.io/MinerU/zh/usage/acceleration_cards/MooreThreads/) - [天数智芯 IluvatarCorex](https://opendatalab.github.io/MinerU/zh/usage/acceleration_cards/IluvatarCorex/) - [寒武纪 Cambricon](https://opendatalab.github.io/MinerU/zh/usage/acceleration_cards/Cambricon/) - [昆仑芯 Kunlunxin](https://opendatalab.github.io/MinerU/zh/usage/acceleration_cards/Kunlunxin/) - [太初元碁 Tecorigin](https://opendatalab.github.io/MinerU/zh/usage/acceleration_cards/Tecorigin/) - [壁仞 Biren](https://opendatalab.github.io/MinerU/zh/usage/acceleration_cards/Biren/) - MinerU 持续兼容国产硬件平台，支持主流芯片架构。以安全可靠的技术，助力科研、政企用户迈向文档数字化新高度！ - 2026/01/30 2.7.4 发布 - 新增国产算力平台天数智芯、寒武纪的适配支持。 - 2026/01/23 2.7.2 发布 - 新增国产算力平台海光、燧原、摩尔线程的适配支持 - 跨页表合并优化，提升合并成功率与合并效果 - 2026/01/06 2.7.1 发布 - fix bug: #4300 - 更新pdfminer.six的依赖版本以解决 [CVE-2025-64512](https://github.com/advisories/GHSA-wf5f-4jwr-ppcp) - 支持输入图像的exif方向自动校正，提升OCR识别效果 #4283 - 2025/12/30 2.7.0 发布 - 简化安装流程，现在不再需要单独安装`vlm`加速引擎依赖包，安装时使用`uv pip install mineru[all]`即可安装所有可选后端的依赖包。 - 增加全新后端`hybrid`，该后端结合了`pipeline`和`vlm`后端的优势，在vlm的基础上，融入了pipeline的部分能力，在高精度的基础上增加了额外的扩展性： - 从文本pdf中直接抽取文本，在文本pdf场景原生支持多语言识别，并极大减少解析幻觉； - 通过指定ocr语言，在扫描pdf场景下支持109种语言的文本识别； - 独立的行内公式识别开关，在不需要行内公式识别的场景下可单独关闭，提升解析结果视觉效果。 - 简化`vlm/hybrid`后端的引擎选择逻辑，用户只需指定后端为`*-auto-engine`，系统会根据当前环境自动选择合适的引擎进行推理加速，提升易用性. - 默认解析后端从`pipeline`切换至`hybrid-auto-engine`，提升新用户开箱即用的结果一致性，避免出现解析结果认知差异。 - gradio应用增加i18n适配，支持中英文两种语言切换。 > 📝 查看完整的 [更新日志](https://opendatalab.github.io/MinerU/zh/reference/changelog/) 了解更多历史版本信息 # MinerU ## 项目简介 MinerU是一款将PDF转化为机器可读格式的工具（如markdown、json），可以很方便地抽取为任意格式。 MinerU诞生于[书生-浦语](https://github.com/InternLM/InternLM)的预训练过程中，我们将会集中精力解决科技文献中的符号转化问题，希望在大模型时代为科技发展做出贡献。 相比国内外知名商用产品MinerU还很年轻，如果遇到问题或者结果不及预期请到[issue](https://github.com/opendatalab/MinerU/issues)提交问题，同时**附上相关PDF**。 https://github.com/user-attachments/assets/4bea02c9-6d54-4cd6-97ed-dff14340982c ## 主要功能 - 删除页眉、页脚、脚注、页码等元素，确保语义连贯 - 输出符合人类阅读顺序的文本，适用于单栏、多栏及复杂排版 - 保留原文档的结构，包括标题、段落、列表等 - 提取图像、图片描述、表格、表格标题及脚注 - 自动识别并转换文档中的公式为LaTeX格式 - 自动识别并转换文档中的表格为HTML格式 - 自动检测扫描版PDF和乱码PDF，并启用OCR功能 - OCR支持109种语言的检测与识别 - 支持多种输出格式，如多模态与NLP的Markdown、按阅读顺序排序的JSON、含有丰富信息的中间格式等 - 支持多种可视化结果，包括layout可视化、span可视化等，便于高效确认输出效果与质检 - 支持纯CPU环境运行，并支持 GPU(CUDA)/NPU(CANN)/MPS 加速 - 兼容Windows、Linux和Mac平台 # 快速开始 如果安装或使用中遇到任何问题，请先查询 FAQ
如果遇到解析效果不及预期，参考 Known Issues
## 在线体验 ### 官网在线应用 官网在线版功能与客户端一致，界面美观，功能丰富，需要登录使用 - [![OpenDataLab](https://img.shields.io/badge/webapp_on_mineru.net-blue?logo=data:image/svg+xml;base64,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&labelColor=white)](https://mineru.net/OpenSourceTools/Extractor?source=github) ### 基于Gradio的在线demo 基于gradio开发的webui，界面简洁，仅包含核心解析功能，免登录 - [![ModelScope](https://img.shields.io/badge/Demo_on_ModelScope-purple?logo=data:image/svg+xml;base64,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&labelColor=white)](https://www.modelscope.cn/studios/OpenDataLab/MinerU) - [![HuggingFace](https://img.shields.io/badge/Demo_on_HuggingFace-yellow.svg?logo=data:image/png;base64,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&labelColor=white)](https://huggingface.co/spaces/opendatalab/MinerU) ## 本地部署 > [!WARNING] > **安装前必看——软硬件环境支持说明** > > 为了确保项目的稳定性和可靠性，我们在开发过程中仅对特定的软硬件环境进行优化和测试。这样当用户在推荐的系统配置上部署和运行项目时，能够获得最佳的性能表现和最少的兼容性问题。 > > 通过集中资源和精力于主线环境，我们团队能够更高效地解决潜在的BUG，及时开发新功能。 > > 在非主线环境中，由于硬件、软件配置的多样性，以及第三方依赖项的兼容性问题，我们无法100%保证项目的完全可用性。因此，对于希望在非推荐环境中使用本项目的用户，我们建议先仔细阅读文档以及FAQ，大多数问题已经在FAQ中有对应的解决方案，除此之外我们鼓励社区反馈问题，以便我们能够逐步扩大支持范围。
解析后端 pipeline *-auto-engine *-http-client
hybrid vlm hybrid vlm
后端特性 兼容性好 硬件配置要求较高 适用于OpenAI兼容服务器2
精度指标1 82+ 90+
操作系统 Linux3 / Windows4 / macOS5
纯CPU平台支持 ✅ ❌ ✅
GPU加速支持 Volta及以后架构GPU或Apple Silicon 不需要
显存最低要求 6GB 10GB 8GB 3GB
内存要求 最低16GB以上,推荐32GB以上 最低8GB
磁盘空间要求 20GB以上,推荐使用SSD 至少2GB
python版本 3.10-3.13
1 精度指标为OmniDocBench (v1.5)的End-to-End Evaluation Overall分数，基于`MinerU`最新版本测试 2 兼容OpenAI API的服务器，如通过`vLLM`/`SGLang`/`LMDeploy`等推理框架部署的本地模型服务器或远程模型服务 3 Linux仅支持2019年及以后发行版 4 由于关键依赖`ray`未能在windows平台支持Python 3.13，故仅支持至3.10~3.12版本 5 macOS 需使用14.0以上版本 > [!TIP] > 除以上主流环境与平台外，我们也收录了一些社区用户反馈的其他平台支持情况，详情请参考[其他加速卡适配](https://opendatalab.github.io/MinerU/zh/usage/)。 > 如果您有意将自己的环境适配经验分享给社区，欢迎通过[show-and-tell](https://github.com/opendatalab/MinerU/discussions/categories/show-and-tell)提交或提交PR至[其他加速卡适配](https://github.com/opendatalab/MinerU/tree/master/docs/zh/usage/acceleration_cards)文档。 ### 安装 MinerU #### 使用pip或uv安装MinerU ```bash pip install --upgrade pip -i https://mirrors.aliyun.com/pypi/simple pip install uv -i https://mirrors.aliyun.com/pypi/simple uv pip install -U "mineru[all]" -i https://mirrors.aliyun.com/pypi/simple ``` #### 通过源码安装MinerU ```bash git clone https://github.com/opendatalab/MinerU.git cd MinerU uv pip install -e .[all] -i https://mirrors.aliyun.com/pypi/simple ``` > [!TIP] > `mineru[all]`包含所有核心功能，兼容Windows / Linux / macOS系统，适合绝大多数用户。 > 如果您需要指定vlm模型的推理框架，或是仅准备在边缘设备安装轻量版client端，可以参考文档[扩展模块安装指南](https://opendatalab.github.io/MinerU/zh/quick_start/extension_modules/)。 --- #### 使用docker部署Mineru MinerU提供了便捷的docker部署方式，这有助于快速搭建环境并解决一些棘手的环境兼容问题。 您可以在文档中获取[Docker部署说明](https://opendatalab.github.io/MinerU/zh/quick_start/docker_deployment/)。 --- ### 使用 MinerU >[!TIP] >默认使用托管在`huggingface`的模型进行解析，首次使用时会自动下载所需模型文件，后续使用将直接加载本地缓存的模型。如果您无法访问`huggingface`，可以通过以下命令切换至国内镜像源: >```bash >export MINERU_MODEL_SOURCE=modelscope >``` 如果您的设备满足上表中GPU加速的条件，可以使用简单的命令行进行文档解析: ```bash mineru -p -o ``` 如果您的设备不满足GPU加速条件，可以指定后端为`pipeline`，以在纯CPU环境下运行: ```bash mineru -p -o -b pipeline ``` 您可以通过命令行、API、WebUI等多种方式使用MinerU进行PDF解析，具体使用方法请参考[使用指南](https://opendatalab.github.io/MinerU/zh/usage/)。 # TODO - [x] 基于模型的阅读顺序 - [x] 正文中目录、列表识别 - [x] 表格识别 - [x] 标题分级 - [x] 手写文本识别 - [x] 竖排文本识别 - [x] 拉丁字母重音符号识别 - [x] 正文中代码块识别 - [x] [化学式识别](docs/chemical_knowledge_introduction/introduction.pdf)(https://mineru.net) - [ ] 图表内容识别 # Known Issues - 阅读顺序基于模型对可阅读内容在空间中的分布进行排序，在极端复杂的排版下可能会部分区域乱序 - 对竖排文字的支持较为有限 - 目录和列表通过规则进行识别，少部分不常见的列表形式可能无法识别 - 代码块在layout模型里还没有支持 - 漫画书、艺术图册、小学教材、习题尚不能很好解析 - 表格识别在复杂表格上可能会出现行/列识别错误 - 在小语种PDF上，OCR识别可能会出现字符不准确的情况（如阿拉伯文易混淆字符等） - 部分公式可能会无法在markdown中渲染 # FAQ - 如果您在使用过程中遇到问题，可以先查看[常见问题](https://opendatalab.github.io/MinerU/zh/faq/)是否有解答。 - 如果未能解决您的问题，您也可以使用[DeepWiki](https://deepwiki.com/opendatalab/MinerU)与AI助手交流，这可以解决大部分常见问题。 - 如果您仍然无法解决问题，您可通过[Discord](https://discord.gg/Tdedn9GTXq)或[WeChat](https://mineru.net/community-portal/?aliasId=3c430f94)加入社区，与其他用户和开发者交流。 # All Thanks To Our Contributors # License Information [LICENSE.md](LICENSE.md) 本项目目前部分模型基于YOLO训练，但因其遵循AGPL协议，可能对某些使用场景构成限制。未来版本迭代中，我们计划探索并替换为许可条款更为宽松的模型，以提升用户友好度及灵活性。 # Acknowledgments - [PDF-Extract-Kit](https://github.com/opendatalab/PDF-Extract-Kit) - [DocLayout-YOLO](https://github.com/opendatalab/DocLayout-YOLO) - [UniMERNet](https://github.com/opendatalab/UniMERNet) - [RapidTable](https://github.com/RapidAI/RapidTable) - [TableStructureRec](https://github.com/RapidAI/TableStructureRec) - [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) - [PaddleOCR2Pytorch](https://github.com/frotms/PaddleOCR2Pytorch) - [layoutreader](https://github.com/ppaanngggg/layoutreader) - [xy-cut](https://github.com/Sanster/xy-cut) - [fast-langdetect](https://github.com/LlmKira/fast-langdetect) - [pypdfium2](https://github.com/pypdfium2-team/pypdfium2) - [pdftext](https://github.com/datalab-to/pdftext) - [pdfminer.six](https://github.com/pdfminer/pdfminer.six) - [pypdf](https://github.com/py-pdf/pypdf) - [magika](https://github.com/google/magika) - [vLLM](https://github.com/vllm-project/vllm) - [LMDeploy](https://github.com/InternLM/lmdeploy) # Citation ```bibtex @article{niu2025mineru2, title={Mineru2. 5: A decoupled vision-language model for efficient high-resolution document parsing}, author={Niu, Junbo and Liu, Zheng and Gu, Zhuangcheng and Wang, Bin and Ouyang, Linke and Zhao, Zhiyuan and Chu, Tao and He, Tianyao and Wu, Fan and Zhang, Qintong and others}, journal={arXiv preprint arXiv:2509.22186}, year={2025} } @article{wang2024mineru, title={Mineru: An open-source solution for precise document content extraction}, author={Wang, Bin and Xu, Chao and Zhao, Xiaomeng and Ouyang, Linke and Wu, Fan and Zhao, Zhiyuan and Xu, Rui and Liu, Kaiwen and Qu, Yuan and Shang, Fukai and others}, journal={arXiv preprint arXiv:2409.18839}, year={2024} } @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 Star History Chart # Links - [Easy Data Preparation with latest LLMs-based Operators and Pipelines](https://github.com/OpenDCAI/DataFlow) - [Vis3 (OSS browser based on s3)](https://github.com/opendatalab/Vis3) - [LabelU (A Lightweight Multi-modal Data Annotation Tool)](https://github.com/opendatalab/labelU) - [LabelLLM (An Open-source LLM Dialogue Annotation Platform)](https://github.com/opendatalab/LabelLLM) - [PDF-Extract-Kit (A Comprehensive Toolkit for High-Quality PDF Content Extraction)](https://github.com/opendatalab/PDF-Extract-Kit) - [OmniDocBench (A Comprehensive Benchmark for Document Parsing and Evaluation)](https://github.com/opendatalab/OmniDocBench) - [Magic-HTML (Mixed web page extraction tool)](https://github.com/opendatalab/magic-html) - [Magic-Doc (Fast speed ppt/pptx/doc/docx/pdf extraction tool)](https://github.com/InternLM/magic-doc) - [Dingo: A Comprehensive AI Data Quality Evaluation Tool](https://github.com/MigoXLab/dingo)