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Merge branch 'master' of github.com:papayalove/Magic-PDF

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README_zh-CN.md
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......@@ -99,7 +99,7 @@ conda activate MinerU
>
> 如需在生产环境使用CUDA/MPS加速请参考[使用CUDA或MPS加速推理](#4-使用CUDA或MPS加速推理)
```bash
pip install magic-pdf[full-cpu]
pip install magic-pdf[full-cpu] -i https://pypi.tuna.tsinghua.edu.cn/simple
```
> ❗️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
> ```bash
......@@ -111,7 +111,7 @@ pip install magic-pdf[full-cpu]
或是直接使用我们预编译的whl包：
> ❗️预编译版本仅支持64位系统(windows/linux/macOS)+pyton 3.10平台；不支持任何32位系统和非mac的arm平台，如系统不支持请自行编译安装。
```bash
pip install detectron2 --extra-index-url https://myhloli.github.io/wheels/
pip install detectron2 --extra-index-url https://myhloli.github.io/wheels/ -i https://pypi.tuna.tsinghua.edu.cn/simple
```
#### 2. 下载模型权重文件
......
README_zh-CN_v2.md 0 → 100644
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<div align="center" xmlns="http://www.w3.org/1999/html">
<!-- logo -->
<p align="center">
<img src="docs/images/MinerU-logo.png" width="300px" style="vertical-align:middle;">
</p>
<!-- icon -->
[![stars](https://img.shields.io/github/stars/opendatalab/MinerU.svg)](https://github.com/opendatalab/MinerU)
[![forks](https://img.shields.io/github/forks/opendatalab/MinerU.svg)](https://github.com/opendatalab/MinerU)
[![open issues](https://img.shields.io/github/issues-raw/opendatalab/MinerU)](https://github.com/opendatalab/MinerU/issues)
[![issue resolution](https://img.shields.io/github/issues-closed-raw/opendatalab/MinerU)](https://github.com/opendatalab/MinerU/issues)
[![PyPI version](https://badge.fury.io/py/magic-pdf.svg)](https://badge.fury.io/py/magic-pdf)
[![Downloads](https://static.pepy.tech/badge/magic-pdf)](https://pepy.tech/project/magic-pdf)
[![Downloads](https://static.pepy.tech/badge/magic-pdf/month)](https://pepy.tech/project/magic-pdf)
<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>
<!-- language -->
[English](README.md) | [简体中文](README_zh-CN.md) | [日本語](README_ja-JP.md)
<!-- hot link -->
<p align="center">
<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>
<a href="https://github.com/opendatalab/PDF-Extract-Kit">PDF-Extract-Kit: 高质量PDF解析工具箱</a>🔥🔥🔥
</p>
<!-- join us -->
<p align="center">
👋 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>
</p>
</div>
# 更新记录
- 2024/07/18 首次开源
<!-- TABLE OF CONTENT -->
<details open="open">
<summary><h2 style="display: inline-block">文档目录</h2></summary>
<ol>
<li>
<a href="#mineru">MinerU</a>
<ul>
<li><a href="#项目简介">项目简介</a></li>
<li><a href="#主要功能">主要功能</a></li>
<li><a href="#快速开始">快速开始</a>
<ul>
<li><a href="#在线体验">在线体验</a></li>
<li><a href="#使用cpu快速体验">使用CPU快速体验</a></li>
<li><a href="#使用gpu">使用GPU</a></li>
</ul>
</li>
<li><a href="#使用">使用方式</a>
<ul>
<li><a href="#命令行">命令行</a></li>
<li><a href="#api">API</a></li>
<li><a href="#二次开发">二次开发指南</a></li>
</ul>
</li>
</ul>
</li>
<li><a href="#todo">TODO List</a></li>
<li><a href="#known-issue">Known Issue</a></li>
<li><a href="#faq">FAQ</a></li>
<li><a href="#all-thanks-to-our-contributors">Contributors</a></li>
<li><a href="#license-information">License Information</a></li>
<li><a href="#acknowledgments">Acknowledgements</a></li>
<li><a href="#citation">Citation</a></li>
<li><a href="#star-history">Star History</a></li>
<li><a href="#magic-doc">magic-doc快速提取PPT/DOC/PDF</a></li>
<li><a href="#magic-html">magic-html提取混合网页内容</a></li>
<li><a href="#links">Links</a></li>
</ol>
</details>
# 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，以此在大模型时代为科技发展做出一点贡献。
## 主要功能
- 删除页眉、页脚、脚注、页码等元素，保持语义连贯
- 对多栏输出符合人类阅读顺序的文本
- 保留原文档的结构，包括标题、段落、列表等
- 提取图像、图片标题、表格、表格标题
- 自动识别文档中的公式并将公式转换成latex
- 乱码PDF自动检测并启用OCR
- 支持CPU和GPU环境
- 支持windows/linux/mac平台
## 快速开始
如果遇到任何安装问题，请先查询 <a href="#faq">FAQ</a> </br>
如果遇到解析效果不及预期，参考 <a href="#known-issue">Known Issue</a></br>
有3种不同方式可以体验MinerU的效果：
- 在线体验(无需任何安装)
- 使用CPU快速体验（Windows，Linux，Mac）
- Linux/Windows + GPU
**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。
<table>
<tr>
<td colspan="3" rowspan="2">操作系统</td>
</tr>
<tr>
<td>Ubuntu 22.04 LTS</td>
<td>Windows 10 / 11</td>
<td>macOS 11+</td>
</tr>
<tr>
<td colspan="3">CPU</td>
<td>x86_64</td>
<td>x86_64</td>
<td>x86_64 / arm64</td>
</tr>
<tr>
<td colspan="3">内存</td>
<td colspan="3">大于等于16GB，推荐32G以上</td>
</tr>
<tr>
<td colspan="3">python版本</td>
<td colspan="3">3.10</td>
</tr>
<tr>
<td colspan="3">Nvidia Driver 版本</td>
<td>latest(专有驱动)</td>
<td>latest</td>
<td>None</td>
</tr>
<tr>
<td colspan="3">CUDA环境</td>
<td>自动安装[12.1(pytorch)+11.8(paddle)]</td>
<td>11.8(手动安装)+cuDNN v8.7.0(手动安装)</td>
<td>None</td>
</tr>
<tr>
<td rowspan="2">GPU硬件支持列表</td>
<td colspan="2">最低要求 8G+显存</td>
<td colspan="2">3060ti/3070/3080/3080ti/4060/4070/4070ti<br>
8G显存仅可开启lavout和公式识别加速</td>
<td rowspan="2">None</td>
</tr>
<tr>
<td colspan="2">推荐配置 16G+显存</td>
<td colspan="2">3090/3090ti/4070tisuper/4080/4090<br>
16G及以上可以同时开启layout，公式识别和ocr加速</td>
</tr>
</table>
### 在线体验
[在线体验点击这里](TODO)
### 使用CPU快速体验
```bash
pip install magic-pdf[full] detectron2 --extra-index-url https://myhloli.github.io/wheels/ -i https://pypi.tuna.tsinghua.edu.cn/simple
```
> ❗️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
> ```bash
> magic-pdf --version
> ```
> 如版本低于0.6.2，请提交issue进行反馈。
### 使用GPU
- [Ubuntu22.04LTS + GPU](docs/README_Ubuntu_CUDA_Acceleration_zh_CN.md)
- [Windows10/11 + GPU](docs/README_Windows_CUDA_Acceleration_zh_CN.md)
## 使用
### 命令行
TODO
### API
处理本地磁盘上的文件
```python
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")
```
处理对象存储上的文件
```python
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")
```
详细实现可参考
- [demo.py 最简单的处理方式](demo/demo.py)
- [magic_pdf_parse_main.py 能够更清晰看到处理流程](demo/magic_pdf_parse_main.py)
### 二次开发
TODO
# TODO
- [ ] 基于语义的阅读顺序
- [ ] 正文中列表识别
- [ ] 正文中代码块识别
- [ ] 目录识别
- [ ] 表格识别
- [ ] 化学式识别
- [ ] 几何图形识别
# Known Issue
- 阅读顺序基于规则的分割，在一些情况下会乱序
- 列表、代码块、目录在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
好消息是，这些我们正在努力实现！
# FAQ
[常见问题](docs/FAQ_zh_cn.md)
[FAQ](docs/FAQ.md)
# All Thanks To Our Contributors
<a href="https://github.com/opendatalab/MinerU/graphs/contributors">
<img src="https://contrib.rocks/image?repo=opendatalab/MinerU" />
</a>
# License Information
[LICENSE.md](LICENSE.md)
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.
# Acknowledgments
- [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)
- [PyMuPDF](https://github.com/pymupdf/PyMuPDF)
- [fast-langdetect](https://github.com/LlmKira/fast-langdetect)
- [pdfminer.six](https://github.com/pdfminer/pdfminer.six)
# Citation
```bibtex
@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}
}
@misc{2024mineru,
title={MinerU: A One-stop, Open-source, High-quality Data Extraction Tool},
author={MinerU Contributors},
howpublished = {\url{https://github.com/opendatalab/MinerU}},
year={2024}
}
```
# Star History
<a>
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=opendatalab/MinerU&type=Date&theme=dark" />
<source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=opendatalab/MinerU&type=Date" />
<img alt="Star History Chart" src="https://api.star-history.com/svg?repos=opendatalab/MinerU&type=Date" />
</picture>
</a>
# Magic-doc
[Magic-Doc](https://github.com/InternLM/magic-doc) Fast speed ppt/pptx/doc/docx/pdf extraction tool
# Magic-html
[Magic-HTML](https://github.com/opendatalab/magic-html) Mixed web page extraction tool
# Links
- [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)
docs/how_to_download_models_zh_cn.md
View file @ 7b712c40
......@@ -33,6 +33,22 @@ model_dir = snapshot_download('wanderkid/PDF-Extract-Kit')
#### Git下载
也可以使用git clone从 ModelScope 下载模型:
需要先安装git lfs
>##### On Linux
>
>Debian and RPM packages are available from packagecloud, see the [Linux installation instructions](INSTALLING.md).
>
>##### On macOS
>
>[Homebrew](https://brew.sh) bottles are distributed and can be installed via `brew install git-lfs`.
>
>##### On Windows
>
>Git LFS is included in the distribution of [Git for Windows](https://gitforwindows.org/).
>Alternatively, you can install a recent version of Git LFS from the [Chocolatey](https://chocolatey.org/) package manager.
然后通过git clone下载模型:
```bash
git clone https://www.modelscope.cn/wanderkid/PDF-Extract-Kit.git
```
......
magic_pdf/dict2md/ocr_mkcontent.py
View file @ 7b712c40
......@@ -214,28 +214,32 @@ def para_to_standard_format(para, img_buket_path):
return para_content
def para_to_standard_format_v2(para_block, img_buket_path):
def para_to_standard_format_v2(para_block, img_buket_path, page_idx):
para_type = para_block['type']
if para_type == BlockType.Text:
para_content = {
'type': 'text',
'text': merge_para_with_text(para_block),
'page_idx': page_idx
}
elif para_type == BlockType.Title:
para_content = {
'type': 'text',
'text': merge_para_with_text(para_block),
'text_level': 1
'text_level': 1,
'page_idx': page_idx
}
elif para_type == BlockType.InterlineEquation:
para_content = {
'type': 'equation',
'text': merge_para_with_text(para_block),
'text_format': "latex"
'text_format': "latex",
'page_idx': page_idx
}
elif para_type == BlockType.Image:
para_content = {
'type': 'image',
'page_idx': page_idx
}
for block in para_block['blocks']:
if block['type'] == BlockType.ImageBody:
......@@ -245,6 +249,7 @@ def para_to_standard_format_v2(para_block, img_buket_path):
elif para_type == BlockType.Table:
para_content = {
'type': 'table',
'page_idx': page_idx
}
for block in para_block['blocks']:
if block['type'] == BlockType.TableBody:
......@@ -352,6 +357,7 @@ def union_make(pdf_info_dict: list, make_mode: str, drop_mode: str, img_buket_pa
raise Exception(f"drop_mode can not be null")
paras_of_layout = page_info.get("para_blocks")
page_idx = page_info.get("page_idx")
if not paras_of_layout:
continue
if make_mode == MakeMode.MM_MD:
......@@ -362,7 +368,7 @@ def union_make(pdf_info_dict: list, make_mode: str, drop_mode: str, img_buket_pa
output_content.extend(page_markdown)
elif make_mode == MakeMode.STANDARD_FORMAT:
for para_block in paras_of_layout:
para_content = para_to_standard_format_v2(para_block, img_buket_path)
para_content = para_to_standard_format_v2(para_block, img_buket_path, page_idx)
output_content.append(para_content)
if make_mode in [MakeMode.MM_MD, MakeMode.NLP_MD]:
return '\n\n'.join(output_content)
......
magic_pdf/model/doc_analyze_by_custom_model.py
View file @ 7b712c40
......@@ -68,6 +68,8 @@ def custom_model_init(ocr: bool = False, show_log: bool = False):
model = None
if model_config.__model_mode__ == "lite":
logger.warning("The Lite mode is provided for developers to conduct testing only, and the output quality is "
"not guaranteed to be reliable.")
model = MODEL.Paddle
elif model_config.__model_mode__ == "full":
model = MODEL.PEK
......
setup.py
View file @ 7b712c40
......@@ -36,7 +36,7 @@ if __name__ == '__main__':
"paddlepaddle==3.0.0b1;platform_system=='Linux'",
"paddlepaddle==2.6.1;platform_system=='Windows' or platform_system=='Darwin'",
],
"full": ["unimernet",
"full": ["unimernet==0.1.6",
"matplotlib",
"ultralytics",
"paddleocr==2.7.3",
......
signatures/version1/cla.json
View file @ 7b712c40
......@@ -7,6 +7,14 @@
"created_at": "2024-07-28T15:55:21Z",
"repoId": 765083837,
"pullRequestNo": 231
},
{
"name": "nutshellfool",
"id": 1439114,
"comment_id": 2259763094,
"created_at": "2024-07-31T06:24:39Z",
"repoId": 765083837,
"pullRequestNo": 258
}
]
}
\ No newline at end of file
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