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+<div align="center" xmlns="http://www.w3.org/1999/html">
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+<!-- logo -->
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+<p align="center">
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+ <img src="docs/images/MinerU-logo.png" width="300px" style="vertical-align:middle;">
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+</p>
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+
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+
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+<!-- icon -->
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+[](https://github.com/opendatalab/MinerU)
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+[](https://github.com/opendatalab/MinerU)
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+[](https://github.com/opendatalab/MinerU/issues)
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+[](https://github.com/opendatalab/MinerU/issues)
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+[](https://badge.fury.io/py/magic-pdf)
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+[](https://pepy.tech/project/magic-pdf)
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+[](https://pepy.tech/project/magic-pdf)
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+<a href="https://trendshift.io/repositories/11174" target="_blank"><img src="https://trendshift.io/api/badge/repositories/11174" alt="opendatalab%2FMinerU | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
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+
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+<!-- language -->
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+[English](README.md) | [简体中文](README_zh-CN.md) | [日本語](README_ja-JP.md)
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+
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+
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+<!-- hot link -->
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+<p align="center">
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+<a href="https://github.com/opendatalab/MinerU">MinerU: 端到端的PDF解析工具（基于PDF-Extract-Kit）支持PDF转Markdown</a>🚀🚀🚀<br>
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+<a href="https://github.com/opendatalab/PDF-Extract-Kit">PDF-Extract-Kit: 高质量PDF解析工具箱</a>🔥🔥🔥
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+</p>
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+
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+<!-- join us -->
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+<p align="center">
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+ 👋 join us on <a href="https://discord.gg/AsQMhuMN" target="_blank">Discord</a> and <a href="https://cdn.vansin.top/internlm/mineru.jpg" target="_blank">WeChat</a>
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+</p>
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+
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+</div>
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+
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+
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+# 更新记录
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+
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+- 2024/07/18 首次开源
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+
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+
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+<!-- TABLE OF CONTENT -->
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+<details open="open">
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+ <summary><h2 style="display: inline-block">文档目录</h2></summary>
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+ <ol>
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+ <li>
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+ <a href="#mineru">MinerU</a>
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+ <ul>
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+ <li><a href="#项目简介">项目简介</a></li>
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+ <li><a href="#主要功能">主要功能</a></li>
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+ <li><a href="#快速开始">快速开始</a>
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+ <ul>
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+ <li><a href="#在线体验">在线体验</a></li>
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+ <li><a href="#使用cpu快速体验">使用CPU快速体验</a></li>
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+ <li><a href="#使用gpu">使用GPU</a></li>
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+ </ul>
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+ </li>
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+ <li><a href="#使用">使用方式</a>
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+ <ul>
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+ <li><a href="#命令行">命令行</a></li>
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+ <li><a href="#api">API</a></li>
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+ <li><a href="#二次开发">二次开发指南</a></li>
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+ </ul>
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+ </li>
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+ </ul>
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+ </li>
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+ <li><a href="#todo">TODO List</a></li>
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+ <li><a href="#known-issue">Known Issue</a></li>
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+ <li><a href="#faq">FAQ</a></li>
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+ <li><a href="#all-thanks-to-our-contributors">Contributors</a></li>
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+ <li><a href="#license-information">License Information</a></li>
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+ <li><a href="#acknowledgments">Acknowledgements</a></li>
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+ <li><a href="#citation">Citation</a></li>
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+ <li><a href="#star-history">Star History</a></li>
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+ <li><a href="#magic-doc">magic-doc快速提取PPT/DOC/PDF</a></li>
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+ <li><a href="#magic-html">magic-html提取混合网页内容</a></li>
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+ <li><a href="#links">Links</a></li>
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+ </ol>
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+</details>
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+
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+
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+
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+# MinerU
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+## 项目简介
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+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。
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+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，以此在大模型时代为科技发展做出一点贡献。
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+
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+## 主要功能
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+
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+- 删除页眉、页脚、脚注、页码等元素，保持语义连贯
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+- 对多栏输出符合人类阅读顺序的文本
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+- 保留原文档的结构，包括标题、段落、列表等
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+- 提取图像、图片标题、表格、表格标题
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+- 自动识别文档中的公式并将公式转换成latex
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+- 乱码PDF自动检测并启用OCR
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+- 支持CPU和GPU环境
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+- 支持windows/linux/mac平台
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+
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+
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+## 快速开始
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+
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+如果遇到任何安装问题，请先查询 <a href="#faq">FAQ</a> </br>
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+如果遇到解析效果不及预期，参考 <a href="#known-issue">Known Issue</a></br>
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+有3种不同方式可以体验MinerU的效果：
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+- 在线体验(无需任何安装)
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+- 使用CPU快速体验（Windows，Linux，Mac）
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+- Linux/Windows + GPU
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+
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+
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+**软硬件环境支持说明**
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+
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+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。
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+
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+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。
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+
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+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。
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+
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+<table>
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+ <tr>
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+ <td colspan="3" rowspan="2">操作系统</td>
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+ </tr>
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+ <tr>
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+ <td>Ubuntu 22.04 LTS</td>
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+ <td>Windows 10 / 11</td>
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+ <td>macOS 11+</td>
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+ </tr>
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+ <tr>
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+ <td colspan="3">CPU</td>
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+ <td>x86_64</td>
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+ <td>x86_64</td>
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+ <td>x86_64 / arm64</td>
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+ </tr>
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+ <tr>
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+ <td colspan="3">内存</td>
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+ <td colspan="3">大于等于16GB，推荐32G以上</td>
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+ </tr>
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+ <tr>
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+ <td colspan="3">python版本</td>
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+ <td colspan="3">3.10</td>
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+ </tr>
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+ <tr>
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+ <td colspan="3">Nvidia Driver 版本</td>
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+ <td>latest(专有驱动)</td>
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+ <td>latest</td>
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+ <td>None</td>
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+ </tr>
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+ <tr>
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+ <td colspan="3">CUDA环境</td>
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+ <td>自动安装[12.1(pytorch)+11.8(paddle)]</td>
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+ <td>11.8(手动安装)+cuDNN v8.7.0(手动安装)</td>
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+ <td>None</td>
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+ </tr>
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+ <tr>
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+ <td rowspan="2">GPU硬件支持列表</td>
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+ <td colspan="2">最低要求 8G+显存</td>
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+ <td colspan="2">3060ti/3070/3080/3080ti/4060/4070/4070ti<br>
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+ 8G显存仅可开启lavout和公式识别加速</td>
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+ <td rowspan="2">None</td>
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+ </tr>
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+ <tr>
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+ <td colspan="2">推荐配置 16G+显存</td>
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+ <td colspan="2">3090/3090ti/4070tisuper/4080/4090<br>
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+ 16G及以上可以同时开启layout，公式识别和ocr加速</td>
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+ </tr>
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+</table>
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+
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+### 在线体验
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+
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+[在线体验点击这里](TODO)
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+
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+
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+### 使用CPU快速体验
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+
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+
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+```bash
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+pip install magic-pdf[full] detectron2 --extra-index-url https://myhloli.github.io/wheels/ -i https://pypi.tuna.tsinghua.edu.cn/simple
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+```
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+
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+> ❗️已收到多起由于镜像源和依赖冲突问题导致安装了错误版本软件包的反馈，请务必安装完成后通过以下命令验证版本是否正确
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+> ```bash
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+> magic-pdf --version
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+> ```
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+> 如版本低于0.6.2，请提交issue进行反馈。
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+
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+### 使用GPU
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+- [Ubuntu22.04LTS + GPU](docs/README_Ubuntu_CUDA_Acceleration_zh_CN.md)
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+- [Windows10/11 + GPU](docs/README_Windows_CUDA_Acceleration_zh_CN.md)
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+
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+
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+## 使用
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+
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+### 命令行
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+
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+TODO
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+
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+### API
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+
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+处理本地磁盘上的文件
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+```python
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+image_writer = DiskReaderWriter(local_image_dir)
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+image_dir = str(os.path.basename(local_image_dir))
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+jso_useful_key = {"_pdf_type": "", "model_list": []}
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+pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer)
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+pipe.pipe_classify()
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+pipe.pipe_analyze()
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+pipe.pipe_parse()
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+md_content = pipe.pipe_mk_markdown(image_dir, drop_mode="none")
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+```
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+
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+处理对象存储上的文件
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+```python
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+s3pdf_cli = S3ReaderWriter(pdf_ak, pdf_sk, pdf_endpoint)
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+image_dir = "s3://img_bucket/"
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+s3image_cli = S3ReaderWriter(img_ak, img_sk, img_endpoint, parent_path=image_dir)
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+pdf_bytes = s3pdf_cli.read(s3_pdf_path, mode=s3pdf_cli.MODE_BIN)
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+jso_useful_key = {"_pdf_type": "", "model_list": []}
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+pipe = UNIPipe(pdf_bytes, jso_useful_key, s3image_cli)
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+pipe.pipe_classify()
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+pipe.pipe_analyze()
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+pipe.pipe_parse()
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+md_content = pipe.pipe_mk_markdown(image_dir, drop_mode="none")
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+```
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+
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+详细实现可参考
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+- [demo.py 最简单的处理方式](demo/demo.py)
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+- [magic_pdf_parse_main.py 能够更清晰看到处理流程](demo/magic_pdf_parse_main.py)
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+
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+
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+### 二次开发
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+
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+TODO
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+
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+# TODO
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+
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+- [ ] 基于语义的阅读顺序
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+- [ ] 正文中列表识别
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+- [ ] 正文中代码块识别
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+- [ ] 目录识别
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+- [ ] 表格识别
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+- [ ] 化学式识别
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+- [ ] 几何图形识别
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+
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+
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+# Known Issue
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+- 阅读顺序基于规则的分割，在一些情况下会乱序
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+- 列表、代码块、目录在layout模型里还没有支持
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+- 漫画书、艺术图册、小学教材、习题尚不能很好解析
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+
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+好消息是，这些我们正在努力实现！
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+
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+# FAQ
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+[常见问题](docs/FAQ_zh_cn.md)
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+[FAQ](docs/FAQ.md)
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+
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+
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+# All Thanks To Our Contributors
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+
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+<a href="https://github.com/opendatalab/MinerU/graphs/contributors">
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+ <img src="https://contrib.rocks/image?repo=opendatalab/MinerU" />
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+</a>
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+
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+# License Information
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+
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+[LICENSE.md](LICENSE.md)
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+
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+The project currently leverages PyMuPDF to deliver advanced functionalities; however, its adherence to the AGPL license may impose limitations on certain use cases. In upcoming iterations, we intend to explore and transition to a more permissively licensed PDF processing library to enhance user-friendliness and flexibility.
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+
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+# Acknowledgments
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+
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+- [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)
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+- [PyMuPDF](https://github.com/pymupdf/PyMuPDF)
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+- [fast-langdetect](https://github.com/LlmKira/fast-langdetect)
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+- [pdfminer.six](https://github.com/pdfminer/pdfminer.six)
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+
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+# Citation
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+
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+```bibtex
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+@article{he2024opendatalab,
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+ title={Opendatalab: Empowering general artificial intelligence with open datasets},
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+ author={He, Conghui and Li, Wei and Jin, Zhenjiang and Xu, Chao and Wang, Bin and Lin, Dahua},
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+ journal={arXiv preprint arXiv:2407.13773},
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+ year={2024}
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+}
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+
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+@misc{2024mineru,
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+ title={MinerU: A One-stop, Open-source, High-quality Data Extraction Tool},
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+ author={MinerU Contributors},
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+ howpublished = {\url{https://github.com/opendatalab/MinerU}},
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+ year={2024}
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+}
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+```
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+
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+# Star History
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+
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+<a>
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+ <picture>
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+ <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=opendatalab/MinerU&type=Date&theme=dark" />
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+ <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=opendatalab/MinerU&type=Date" />
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+ <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=opendatalab/MinerU&type=Date" />
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+ </picture>
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+</a>
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+
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+# Magic-doc
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+[Magic-Doc](https://github.com/InternLM/magic-doc) Fast speed ppt/pptx/doc/docx/pdf extraction tool
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+
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+# Magic-html
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+[Magic-HTML](https://github.com/opendatalab/magic-html) Mixed web page extraction tool
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+
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+# Links
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+
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+- [LabelU (A Lightweight Multi-modal Data Annotation Tool)](https://github.com/opendatalab/labelU)
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+- [LabelLLM (An Open-source LLM Dialogue Annotation Platform)](https://github.com/opendatalab/LabelLLM)
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+- [PDF-Extract-Kit (A Comprehensive Toolkit for High-Quality PDF Content Extraction)](https://github.com/opendatalab/PDF-Extract-Kit)
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+
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+
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+
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+
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+
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+
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liukaiwen