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*.js linguist-vendored
*.mjs linguist-vendored
*.html linguist-documentation
*.css linguist-vendored
*.scss linguist-vendored
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*.tar
*.tar.gz
*.zip
venv*/
envs/
slurm_logs/
sync1.sh
data_preprocess_pj1
data-preparation1
__pycache__
*.log
*.pyc
.vscode
debug/
*.ipynb
.idea
# vscode history
.history
.DS_Store
.env
bad_words/
bak/
app/tests/*
temp/
tmp/
tmp
.vscode
.vscode/
ocr_demo
.coveragerc
/app/common/__init__.py
/magic_pdf/config/__init__.py
source.dev.env
tmp
projects/web/node_modules
projects/web/dist
projects/web_demo/web_demo/static/
cli_debug/
debug_utils/
# sphinx docs
_build/
output/
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GNU AFFERO GENERAL PUBLIC LICENSE
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If your software can interact with users remotely through a computer
network, you should also make sure that it provides a way for users to
get its source. For example, if your program is a web application, its
interface could display a "Source" link that leads users to an archive
of the code. There are many ways you could offer source, and different
solutions will be better for different programs; see section 13 for the
specific requirements.
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU AGPL, see
<https://www.gnu.org/licenses/>.
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# MinerU Contributor License Agreement
In order to clarify the intellectual property license granted with Contributions from any person or entity, the open source project MinerU ("MinerU") must have a Contributor License Agreement (CLA) on file that has been signed by each Contributor, indicating agreement to the license terms below. This license is for your protection as a Contributor as well as the protection of MinerU and its users; it does not change your rights to use your own Contributions for any other purpose.
You accept and agree to the following terms and conditions for Your present and future Contributions submitted to MinerU. Except for the license granted herein to MinerU and recipients of software distributed by MinerU, You reserve all right, title, and interest in and to Your Contributions.
1. Definitions. "You" (or "Your") shall mean the copyright owner or legal entity authorized by the copyright owner that is making this Agreement with MinerU. For legal entities, the entity making a Contribution and all other entities that control, are controlled by, or are under common control with that entity are considered to be a single Contributor. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "Contribution" shall mean the code, documentation or any original work of authorship, including any modifications or additions to an existing work, that is intentionally submitted by You to MinerU for inclusion in, or documentation of, any of the products owned or managed by MinerU (the "Work"). For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to MinerU or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, MinerU for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by You as "Not a Contribution."
2. Grant of Copyright License. Subject to the terms and conditions of this Agreement, You hereby grant to MinerU and to recipients of software distributed by MinerU a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare derivative works of, publicly display, publicly perform, sublicense, and distribute Your Contributions and such derivative works.
3. Grant of Patent License. Subject to the terms and conditions of this Agreement, You hereby grant to MinerU and to recipients of software distributed by MinerU a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by You that are necessarily infringed by Your Contribution(s) alone or by combination of Your Contribution(s) with the Work to which such Contribution(s) was submitted. If any entity institutes patent litigation against You or any other entity (including a cross-claim or counterclaim in a lawsuit) alleging that Your Contribution, or the Work to which You have contributed, constitutes direct or contributory patent infringement, then any patent licenses granted to that entity under this Agreement for that Contribution or Work shall terminate as of the date such litigation is filed.
4. You represent that You are legally entitled to grant the above license. If You are an entity, You represent further that each of Your employee designated by You is authorized to submit Contributions on behalf of You. If You are an individual and Your employer(s) has rights to intellectual property that You create that includes Your Contributions, You represent further that You have received permission to make Contributions on behalf of that employer, that Your employer has waived such rights for Your Contributions to MinerU, or that Your employer has executed a separate CLA with MinerU.
5. If you do post content or submit material on MinerU and unless we indicate otherwise, you grant MinerU a nonexclusive, royalty-free, perpetual, irrevocable, and fully sublicensable right to use, reproduce, modify, adapt, publish, perform, translate, create derivative works from, distribute, and display such content throughout the world in any media. You grant MinerU and sublicensees the right to use your GitHub Public Profile, including but not limited to name, that you submit in connection with such content. You represent and warrant that you own or otherwise control all of the rights to the content that you post; that the content is accurate; that use of the content you supply does not violate this policy and will not cause injury to any person or entity; and that you will indemnify MinerU for all claims resulting from content you supply. MinerU has the right but not the obligation to monitor and edit or remove any activity or content. MinerU takes no responsibility and assumes no liability for any content posted by you or any third party.
6. You represent that each of Your Contributions is Your original creation. Should You wish to submit work that is not Your original creation, You may submit it to MinerU separately from any Contribution, identifying the complete details of its source and of any license or other restriction (including, but not limited to, related patents, trademarks, and license agreements) of which You are personally aware, and conspicuously marking the work as "Submitted on behalf of a third-party: [named here]".
7. You are not expected to provide support for Your Contributions, except to the extent You desire to provide support. You may provide support for free, for a fee, or not at all. Unless required by applicable law or agreed to in writing, You provide Your Contributions on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE.
8. You agree to notify MinerU of any facts or circumstances of which You become aware that would make these representations inaccurate in any respect.
9. MinerU reserves the right to update or change this Agreement at any time, by posting the most current version of the Agreement on MinerU, with a new Effective Date shown on Jul. 24th, 2024. All such changes in the Agreement are effective from the Effective Date. Your continued use of MinerU after we post any such changes signifies your agreement to those changes. If you do not agree to the then-current Agreement, you must immediately discontinue using MinerU.
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# MinerU
## 论文
`
MinerU: An Open-Source Solution for Precise Document Content Extraction
`
- https://arxiv.org/abs/2409.18839
## 模型结构
MinerU是一个功能强大的PDF文档内容提取工具，它利用了先进的PDF-Extract-Kit模型库，能够有效地从各种类型的文档中提取内容。
MinerU的框架设计简洁而高效，主要包括文档预处理、文档内容解析、文档内容后处理和格式转换四个阶段。
<div align=center>
<img src="./assets/workflow.png"/>
</div>
## 算法原理
MinerU处理过程如下：
（1）文档预处理
文档预处理主要有两个目标。一是筛选出无法处理的 PDF 文件，例如非 PDF 格式文件、加密文档以及受密码保护的文件，确保后续处理流程的顺利进行。
二是获取 PDF 文档的元数据，这些元数据在后续的处理过程中具有重要作用。
（2）文档内容解析
PDF - Extract - Kit 是 MinerU 用于解析文档的核心模型库，包含多种先进的开源 PDF 文档解析算法。与其他开源算法库不同，它致力于在处理现实世界多样化数据时确保准确性和速度。
当特定领域的现有开源算法无法满足实际需求时，PDF - Extract - Kit 会通过数据工程构建高质量、多样化的数据集来进一步微调模型，从而显著增强模型对不同数据的鲁棒性。
（3）文档内容后处理
文档内容后处理阶段主要解决内容排序问题。由于模型输出的文本、图像、表格和公式框之间可能存在重叠，
以及通过 OCR 或 API 获得的文本行之间也经常重叠，这给文本和元素的排序带来了巨大挑战。
（4）格式转换
最后，在格式转换阶段，MinerU将处理后的PDF数据转换为用户所需的机器可读格式（如Markdown或JSON）。
## 环境配置
### Docker（方法一）
```
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.4.1-ubuntu22.04-dtk25.04-py3.10-fixpy
# <your IMAGE ID>为以上拉取的docker的镜像ID替换
docker run -it --name mineru --shm-size=1024G --device=/dev/kfd --device=/dev/dri/ --privileged --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --ulimit memlock=-1:-1 --ipc=host --network host --group-add video -v /opt/hyhal:/opt/hyhal:ro -v $PWD/MinerU_pytorch:/home/MinerU_pytorch <your IMAGE ID> /bin/bash
cd /home/MinerU_pytorch
pip install -e .[core] -i https://mirrors.aliyun.com/pypi/simple/
pip install numpy==1.24.3
pip install torchvision-0.19.1+das.opt2.dtk2504-cp310-cp310-manylinux_2_28_x86_64.whl
pip install torch-2.4.1+das.opt2.dtk2504-cp310-cp310-manylinux_2_28_x86_64.whl
pip install triton-3.0.0+das.opt4.dtk2504-cp310-cp310-manylinux_2_28_x86_64.whl
cd sglang-v0.4.6.post5.dev/sgl-kernel
python setup_hip.py install
cd ..
pip install -e "python[all_hip]"
```
### Dockerfile（方法二）
```
cd /home/MinerU_pytorch
docker build --no-cache -t MinerU:latest .
docker run -it --name MinerU_test --shm-size=1024G --device=/dev/kfd --device=/dev/dri/ --privileged --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --ulimit memlock=-1:-1 --ipc=host --network host --group-add video -v /opt/hyhal:/opt/hyhal:ro -v $PWD/MinerU_pytorch:/home/MinerU_pytorch MinerU /bin/bash
cd /home/MinerU_pytorch
pip install -e .[core] -i https://mirrors.aliyun.com/pypi/simple/
pip install numpy==1.24.3
pip install torchvision-0.19.1+das.opt2.dtk2504-cp310-cp310-manylinux_2_28_x86_64.whl
pip install torch-2.4.1+das.opt2.dtk2504-cp310-cp310-manylinux_2_28_x86_64.whl
pip install triton-3.0.0+das.opt4.dtk2504-cp310-cp310-manylinux_2_28_x86_64.whl
cd sglang-v0.4.6.post5.dev/sgl-kernel
python setup_hip.py install
cd ..
pip install -e "python[all_hip]"
```
### Anaconda（方法三）
1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装：
- https://developer.sourcefind.cn/tool/
```
DTK驱动:dtk25.04
python:python3.10
torch:2.4.1
torchvision:0.19.1
triton:3.0.0
flash-attn:2.6.1
vllm:0.7.2
```
`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应。`
2、其它非特殊库参照requirements.txt安装
```
cd /home/MinerU_pytorch
pip install -e .[core] -i https://mirrors.aliyun.com/pypi/simple/
pip install torchvision-0.19.1+das.opt2.dtk2504-cp310-cp310-manylinux_2_28_x86_64.whl
pip install torch-2.4.1+das.opt2.dtk2504-cp310-cp310-manylinux_2_28_x86_64.whl
pip install triton-3.0.0+das.opt4.dtk2504-cp310-cp310-manylinux_2_28_x86_64.whl
pip install lmslim-0.2.1+das.dtk2504-cp310-cp310-manylinux_2_28_x86_64.whl
pip install flash_attn-2.6.1+das.opt4.dtk2504-cp310-cp310-manylinux_2_28_x86_64.whl
pip install vllm-0.7.2+das.opt1.dtk2504-cp310-cp310-manylinux_2_28_x86_64.whl
cd sglang-v0.4.6.post5.dev/sgl-kernel
python setup_hip.py install
cd ..
pip install -e "python[all_hip]"
pip install pytest==8.3.5 pytest-asyncio==0.26.0 numpy==1.24.3
pip install amdsmi-24.5.3+02cbffb.dirty-py3-none-any.whl
```
## 数据集
`无`
## 训练
`无`
## 推理
模型源配置
```
#添加HF镜像方便下载模型
#export HF_ENDPOINT=https://hf-mirror.com
#默认在首次运行时自动从 HuggingFace 下载所需模型。
#若无法访问 HuggingFace，可通过以下方式切换模型源：
mineru -p <input_path> -o <output_path> --source modelscope
#或设置环境变量：
export MINERU_MODEL_SOURCE=modelscope
#如需使用本地模型，可使用交互式命令行工具选择模型下载：
mineru-models-download --help
#下载完成后，模型路径会在当前终端窗口输出，并自动写入用户目录下的 mineru.json
```
### 单机单卡
```
#Run pipeline
cd /home/MinerU_pytorch
HIP_VISIBLE_DEVICES=0 python demo/demo.py
#Using sglang to Accelerate VLM Model Inference
#Through the sglang-server/client Mode
#注意：运行的时候需要加 --attention-backend triton选项，别的kernel不支持
HIP_VISIBLE_DEVICES=0 mineru-sglang-server --attention-backend triton --port 30000
#Use Client in another terminal:
mineru -p <input_path> -o <output_path> -b vlm-sglang-client -u http://127.0.0.1:30000
```
更多资料可参考源项目中的[`README_ori`](./README_orgin.md)。
## result
解析示例：
layout:
<div align=center>
<img src="./assets/layout.png"/>
</div>
解析结果：
<div align=center>
<img src="./assets/result.png"/>
</div>
### 精度
DCU与GPU精度一致，推理框架：pytorch。
## 应用场景
### 算法类别
`OCR`
### 热点应用行业
`科研,教育,政府,广媒`
## 预训练权重
魔搭社区下载地址为：[OpenDataLab/PDF-Extract-Kit-1.0](https://modelscope.cn/models/OpenDataLab/PDF-Extract-Kit-1.0)
Hugging Face下载地址为：[OpenDataLab/PDF-Extract-Kit-1.0](https://huggingface.co/opendatalab/PDF-Extract-Kit-1.0)
注意：`自动下载模型建议加镜像源下载：export HF_ENDPOINT=https://hf-mirror.com`
## 源码仓库及问题反馈
- https://developer.sourcefind.cn/codes/modelzoo/mineru_pytorch
## 参考资料
- https://github.com/opendatalab/MinerU
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<!-- logo -->
<p align="center">
<img src="docs/images/MinerU-logo.png" width="300px" style="vertical-align:middle;">
</p>
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[![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)
[![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)
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[![Paper](https://img.shields.io/badge/Paper-arXiv-green)](https://arxiv.org/abs/2409.18839)
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<!-- language -->
[English](README.md) | [简体中文](README_zh-CN.md)
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<a href="https://github.com/opendatalab/PDF-Extract-Kit">PDF-Extract-Kit: High-Quality PDF Extraction Toolkit</a>🔥🔥🔥
<br>
<br>
<a href="https://mineru.net/client?source=github">
Easier to use: Just grab MinerU Desktop. No coding, no login, just a simple interface and smooth interactions. Enjoy it without any fuss!</a>🚀🚀🚀
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👋 join us on <a href="https://discord.gg/Tdedn9GTXq" target="_blank">Discord</a> and <a href="http://mineru.space/s/V85Yl" target="_blank">WeChat</a>
</p>
</div>
# Changelog
- 2025/06/20 2.0.6 Released
- Fixed occasional parsing interruptions caused by invalid block content in `vlm` mode
- Fixed parsing interruptions caused by incomplete table structures in `vlm` mode
- 2025/06/17 2.0.5 Released
- Fixed the issue where models were still required to be downloaded in the `sglang-client` mode
- Fixed the issue where the `sglang-client` mode unnecessarily depended on packages like `torch` during runtime.
- Fixed the issue where only the first instance would take effect when attempting to launch multiple `sglang-client` instances via multiple URLs within the same process
- 2025/06/15 2.0.3 released
- Fixed a configuration file key-value update error that occurred when downloading model type was set to `all`
- Fixed the issue where the formula and table feature toggle switches were not working in `command line mode`, causing the features to remain enabled.
- Fixed compatibility issues with sglang version 0.4.7 in the `sglang-engine` mode.
- Updated Dockerfile and installation documentation for deploying the full version of MinerU in sglang environment
- 2025/06/13 2.0.0 Released
- MinerU 2.0 represents a comprehensive reconstruction and upgrade from architecture to functionality, delivering a more streamlined design, enhanced performance, and more flexible user experience.
- **New Architecture**: MinerU 2.0 has been deeply restructured in code organization and interaction methods, significantly improving system usability, maintainability, and extensibility.
- **Removal of Third-party Dependency Limitations**: Completely eliminated the dependency on `pymupdf`, moving the project toward a more open and compliant open-source direction.
- **Ready-to-use, Easy Configuration**: No need to manually edit JSON configuration files; most parameters can now be set directly via command line or API.
- **Automatic Model Management**: Added automatic model download and update mechanisms, allowing users to complete model deployment without manual intervention.
- **Offline Deployment Friendly**: Provides built-in model download commands, supporting deployment requirements in completely offline environments.
- **Streamlined Code Structure**: Removed thousands of lines of redundant code, simplified class inheritance logic, significantly improving code readability and development efficiency.
- **Unified Intermediate Format Output**: Adopted standardized `middle_json` format, compatible with most secondary development scenarios based on this format, ensuring seamless ecosystem business migration.
- **New Model**: MinerU 2.0 integrates our latest small-parameter, high-performance multimodal document parsing model, achieving end-to-end high-speed, high-precision document understanding.
- **Small Model, Big Capabilities**: With parameters under 1B, yet surpassing traditional 72B-level vision-language models (VLMs) in parsing accuracy.
- **Multiple Functions in One**: A single model covers multilingual recognition, handwriting recognition, layout analysis, table parsing, formula recognition, reading order sorting, and other core tasks.
- **Ultimate Inference Speed**: Achieves peak throughput exceeding 10,000 tokens/s through `sglang` acceleration on a single NVIDIA 4090 card, easily handling large-scale document processing requirements.
- **Online Experience**: You can experience our brand-new VLM model on [MinerU.net](https://mineru.net/OpenSourceTools/Extractor), [Hugging Face](https://huggingface.co/spaces/opendatalab/MinerU), and [ModelScope](https://www.modelscope.cn/studios/OpenDataLab/MinerU).
- **Incompatible Changes Notice**: To improve overall architectural rationality and long-term maintainability, this version contains some incompatible changes:
- Python package name changed from `magic-pdf` to `mineru`, and the command-line tool changed from `magic-pdf` to `mineru`. Please update your scripts and command calls accordingly.
- For modular system design and ecosystem consistency considerations, MinerU 2.0 no longer includes the LibreOffice document conversion module. If you need to process Office documents, we recommend converting them to PDF format through an independently deployed LibreOffice service before proceeding with subsequent parsing operations.
<details>
<summary>History Log</summary>
<details>
<summary>2025/05/24 Release 1.3.12</summary>
<ul>
<li>Added support for PPOCRv5 models, updated <code>ch_server</code> model to <code>PP-OCRv5_rec_server</code>, and <code>ch_lite</code> model to <code>PP-OCRv5_rec_mobile</code> (model update required)
<ul>
<li>In testing, we found that PPOCRv5(server) has some improvement for handwritten documents, but has slightly lower accuracy than v4_server_doc for other document types, so the default ch model remains unchanged as <code>PP-OCRv4_server_rec_doc</code>.</li>
<li>Since PPOCRv5 has enhanced recognition capabilities for handwriting and special characters, you can manually choose the PPOCRv5 model for Japanese-Traditional Chinese mixed scenarios and handwritten documents</li>
<li>You can select the appropriate model through the lang parameter <code>lang='ch_server'</code> (Python API) or <code>--lang ch_server</code> (command line):
<ul>
<li><code>ch</code>: <code>PP-OCRv4_server_rec_doc</code> (default) (Chinese/English/Japanese/Traditional Chinese mixed/15K dictionary)</li>
<li><code>ch_server</code>: <code>PP-OCRv5_rec_server</code> (Chinese/English/Japanese/Traditional Chinese mixed + handwriting/18K dictionary)</li>
<li><code>ch_lite</code>: <code>PP-OCRv5_rec_mobile</code> (Chinese/English/Japanese/Traditional Chinese mixed + handwriting/18K dictionary)</li>
<li><code>ch_server_v4</code>: <code>PP-OCRv4_rec_server</code> (Chinese/English mixed/6K dictionary)</li>
<li><code>ch_lite_v4</code>: <code>PP-OCRv4_rec_mobile</code> (Chinese/English mixed/6K dictionary)</li>
</ul>
</li>
</ul>
</li>
<li>Added support for handwritten documents through optimized layout recognition of handwritten text areas
<ul>
<li>This feature is supported by default, no additional configuration required</li>
<li>You can refer to the instructions above to manually select the PPOCRv5 model for better handwritten document parsing results</li>
</ul>
</li>
<li>The <code>huggingface</code> and <code>modelscope</code> demos have been updated to versions that support handwriting recognition and PPOCRv5 models, which you can experience online</li>
</ul>
</details>
<details>
<summary>2025/04/29 Release 1.3.10</summary>
<ul>
<li>Added support for custom formula delimiters, which can be configured by modifying the <code>latex-delimiter-config</code> section in the <code>magic-pdf.json</code> file in your user directory.</li>
</ul>
</details>
<details>
<summary>2025/04/27 Release 1.3.9</summary>
<ul>
<li>Optimized formula parsing functionality, improved formula rendering success rate</li>
</ul>
</details>
<details>
<summary>2025/04/23 Release 1.3.8</summary>
<ul>
<li>The default <code>ocr</code> model (<code>ch</code>) has been updated to <code>PP-OCRv4_server_rec_doc</code> (model update required)
<ul>
<li><code>PP-OCRv4_server_rec_doc</code> is trained on a mixture of more Chinese document data and PP-OCR training data based on <code>PP-OCRv4_server_rec</code>, adding recognition capabilities for some traditional Chinese characters, Japanese, and special characters. It can recognize over 15,000 characters and improves both document-specific and general text recognition abilities.</li>
<li><a href="https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/ocr_modules/text_recognition.html#_3">Performance comparison of PP-OCRv4_server_rec_doc/PP-OCRv4_server_rec/PP-OCRv4_mobile_rec</a></li>
<li>After verification, the <code>PP-OCRv4_server_rec_doc</code> model shows significant accuracy improvements in Chinese/English/Japanese/Traditional Chinese in both single language and mixed language scenarios, with comparable speed to <code>PP-OCRv4_server_rec</code>, making it suitable for most use cases.</li>
<li>In some pure English scenarios, <code>PP-OCRv4_server_rec_doc</code> may have word adhesion issues, while <code>PP-OCRv4_server_rec</code> performs better in these cases. Therefore, we've kept the <code>PP-OCRv4_server_rec</code> model, which users can access by adding the parameter <code>lang='ch_server'</code> (Python API) or <code>--lang ch_server</code> (command line).</li>
</ul>
</li>
</ul>
</details>
<details>
<summary>2025/04/22 Release 1.3.7</summary>
<ul>
<li>Fixed the issue where the lang parameter was ineffective during table parsing model initialization</li>
<li>Fixed the significant speed reduction of OCR and table parsing in <code>cpu</code> mode</li>
</ul>
</details>
<details>
<summary>2025/04/16 Release 1.3.4</summary>
<ul>
<li>Slightly improved OCR-det speed by removing some unnecessary blocks</li>
<li>Fixed page-internal sorting errors caused by footnotes in certain cases</li>
</ul>
</details>
<details>
<summary>2025/04/12 Release 1.3.2</summary>
<ul>
<li>Fixed dependency version incompatibility issues when installing on Windows with Python 3.13</li>
<li>Optimized memory usage during batch inference</li>
<li>Improved parsing of tables rotated 90 degrees</li>
<li>Enhanced parsing of oversized tables in financial report samples</li>
<li>Fixed the occasional word adhesion issue in English text areas when OCR language is not specified (model update required)</li>
</ul>
</details>
<details>
<summary>2025/04/08 Release 1.3.1</summary>
<ul>
<li>Fixed several compatibility issues
<ul>
<li>Added support for Python 3.13</li>
<li>Made final adaptations for outdated Linux systems (such as CentOS 7) with no guarantee of continued support in future versions, <a href="https://github.com/opendatalab/MinerU/issues/1004">installation instructions</a></li>
</ul>
</li>
</ul>
</details>
<details>
<summary>2025/04/03 Release 1.3.0</summary>
<ul>
<li>Installation and compatibility optimizations
<ul>
<li>Resolved compatibility issues caused by <code>detectron2</code> by removing <code>layoutlmv3</code> usage in layout</li>
<li>Extended torch version compatibility to 2.2~2.6 (excluding 2.5)</li>
<li>Added CUDA compatibility for versions 11.8/12.4/12.6/12.8 (CUDA version determined by torch), solving compatibility issues for users with 50-series and H-series GPUs</li>
<li>Extended Python compatibility to versions 3.10~3.12, fixing the issue of automatic downgrade to version 0.6.1 when installing in non-3.10 environments</li>
<li>Optimized offline deployment process, eliminating the need to download any model files after successful deployment</li>
</ul>
</li>
<li>Performance optimizations
<ul>
<li>Enhanced parsing speed for batches of small files by supporting batch processing of multiple PDF files (<a href="demo/batch_demo.py">script example</a>), with formula parsing speed improved by up to 1400% and overall parsing speed improved by up to 500% compared to version 1.0.1</li>
<li>Reduced memory usage and improved parsing speed by optimizing MFR model loading and usage (requires re-running the <a href="docs/how_to_download_models_zh_cn.md">model download process</a> to get incremental updates to model files)</li>
<li>Optimized GPU memory usage, requiring only 6GB minimum to run this project</li>
<li>Improved running speed on MPS devices</li>
</ul>
</li>
<li>Parsing effect optimizations
<ul>
<li>Updated MFR model to <code>unimernet(2503)</code>, fixing line break loss issues in multi-line formulas</li>
</ul>
</li>
<li>Usability optimizations
<ul>
<li>Completely replaced the <code>paddle</code> framework and <code>paddleocr</code> in the project by using <code>paddleocr2torch</code>, resolving conflicts between <code>paddle</code> and <code>torch</code>, as well as thread safety issues caused by the <code>paddle</code> framework</li>
<li>Added real-time progress bar display during parsing, allowing precise tracking of parsing progress and making the waiting process more bearable</li>
</ul>
</li>
</ul>
</details>
<details>
<summary>2025/03/03 1.2.1 released</summary>
<ul>
<li>Fixed the impact on punctuation marks during full-width to half-width conversion of letters and numbers</li>
<li>Fixed caption matching inaccuracies in certain scenarios</li>
<li>Fixed formula span loss issues in certain scenarios</li>
</ul>
</details>
<details>
<summary>2025/02/24 1.2.0 released</summary>
<p>This version includes several fixes and improvements to enhance parsing efficiency and accuracy:</p>
<ul>
<li><strong>Performance Optimization</strong>
<ul>
<li>Increased classification speed for PDF documents in auto mode.</li>
</ul>
</li>
<li><strong>Parsing Optimization</strong>
<ul>
<li>Improved parsing logic for documents containing watermarks, significantly enhancing the parsing results for such documents.</li>
<li>Enhanced the matching logic for multiple images/tables and captions within a single page, improving the accuracy of image-text matching in complex layouts.</li>
</ul>
</li>
<li><strong>Bug Fixes</strong>
<ul>
<li>Fixed an issue where image/table spans were incorrectly filled into text blocks under certain conditions.</li>
<li>Resolved an issue where title blocks were empty in some cases.</li>
</ul>
</li>
</ul>
</details>
<details>
<summary>2025/01/22 1.1.0 released</summary>
<p>In this version we have focused on improving parsing accuracy and efficiency:</p>
<ul>
<li><strong>Model capability upgrade</strong> (requires re-executing the <a href="https://github.com/opendatalab/MinerU/blob/master/docs/how_to_download_models_en.md">model download process</a> to obtain incremental updates of model files)
<ul>
<li>The layout recognition model has been upgraded to the latest <code>doclayout_yolo(2501)</code> model, improving layout recognition accuracy.</li>
<li>The formula parsing model has been upgraded to the latest <code>unimernet(2501)</code> model, improving formula recognition accuracy.</li>
</ul>
</li>
<li><strong>Performance optimization</strong>
<ul>
<li>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%.</li>
</ul>
</li>
<li><strong>Parsing effect optimization</strong>
<ul>
<li>Added a new heading classification feature (testing version, enabled by default) to the online demo (<a href="https://mineru.net/OpenSourceTools/Extractor">mineru.net</a>/<a href="https://huggingface.co/spaces/opendatalab/MinerU">huggingface</a>/<a href="https://www.modelscope.cn/studios/OpenDataLab/MinerU">modelscope</a>), which supports hierarchical classification of headings, thereby enhancing document structuring.</li>
</ul>
</li>
</ul>
</details>
<details>
<summary>2025/01/10 1.0.1 released</summary>
<p>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:</p>
<ul>
<li><strong>New API Interface</strong>
<ul>
<li>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.</li>
<li>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.</li>
</ul>
</li>
<li><strong>Enhanced Compatibility</strong>
<ul>
<li>By optimizing the dependency environment and configuration items, we ensure stable and efficient operation on ARM architecture Linux systems.</li>
<li>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. <a href="https://github.com/opendatalab/MinerU/blob/master/docs/README_Ascend_NPU_Acceleration_zh_CN.md">Ascend NPU Acceleration</a></li>
</ul>
</li>
<li><strong>Automatic Language Identification</strong>
<ul>
<li>By introducing a new language recognition model, setting the <code>lang</code> configuration to <code>auto</code> during document parsing will automatically select the appropriate OCR language model, improving the accuracy of scanned document parsing.</li>
</ul>
</li>
</ul>
</details>
<details>
<summary>2024/11/22 0.10.0 released</summary>
<p>Introducing hybrid OCR text extraction capabilities:</p>
<ul>
<li>Significantly improved parsing performance in complex text distribution scenarios such as dense formulas, irregular span regions, and text represented by images.</li>
<li>Combines the dual advantages of accurate content extraction and faster speed in text mode, and more precise span/line region recognition in OCR mode.</li>
</ul>
</details>
<details>
<summary>2024/11/15 0.9.3 released</summary>
<p>Integrated <a href="https://github.com/RapidAI/RapidTable">RapidTable</a> for table recognition, improving single-table parsing speed by more than 10 times, with higher accuracy and lower GPU memory usage.</p>
</details>
<details>
<summary>2024/11/06 0.9.2 released</summary>
<p>Integrated the <a href="https://huggingface.co/U4R/StructTable-InternVL2-1B">StructTable-InternVL2-1B</a> model for table recognition functionality.</p>
</details>
<details>
<summary>2024/10/31 0.9.0 released</summary>
<p>This is a major new version with extensive code refactoring, addressing numerous issues, improving performance, reducing hardware requirements, and enhancing usability:</p>
<ul>
<li>Refactored the sorting module code to use <a href="https://github.com/ppaanngggg/layoutreader">layoutreader</a> for reading order sorting, ensuring high accuracy in various layouts.</li>
<li>Refactored the paragraph concatenation module to achieve good results in cross-column, cross-page, cross-figure, and cross-table scenarios.</li>
<li>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.</li>
<li>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.</li>
<li>Added multi-language support for OCR, supporting detection and recognition of 84 languages. For the list of supported languages, see <a href="https://paddlepaddle.github.io/PaddleOCR/latest/en/ppocr/blog/multi_languages.html#5-support-languages-and-abbreviations">OCR Language Support List</a>.</li>
<li>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.</li>
<li>Optimized configuration file feature switches, adding an independent formula detection switch to significantly improve speed and parsing results when formula detection is not needed.</li>
<li>Integrated <a href="https://github.com/opendatalab/PDF-Extract-Kit">PDF-Extract-Kit 1.0</a>:
<ul>
<li>Added the self-developed <code>doclayout_yolo</code> 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 <code>layoutlmv3</code> via the configuration file.</li>
<li>Upgraded formula parsing to <code>unimernet 0.2.1</code>, improving formula parsing accuracy while significantly reducing memory usage.</li>
<li>Due to the repository change for <code>PDF-Extract-Kit 1.0</code>, you need to re-download the model. Please refer to <a href="https://github.com/opendatalab/MinerU/blob/master/docs/how_to_download_models_en.md">How to Download Models</a> for detailed steps.</li>
</ul>
</li>
</ul>
</details>
<details>
<summary>2024/09/27 Version 0.8.1 released</summary>
<p>Fixed some bugs, and providing a <a href="https://github.com/opendatalab/MinerU/blob/master/projects/web_demo/README.md">localized deployment version</a> of the <a href="https://opendatalab.com/OpenSourceTools/Extractor/PDF/">online demo</a> and the <a href="https://github.com/opendatalab/MinerU/blob/master/projects/web/README.md">front-end interface</a>.</p>
</details>
<details>
<summary>2024/09/09 Version 0.8.0 released</summary>
<p>Supporting fast deployment with Dockerfile, and launching demos on Huggingface and Modelscope.</p>
</details>
<details>
<summary>2024/08/30 Version 0.7.1 released</summary>
<p>Add paddle tablemaster table recognition option</p>
</details>
<details>
<summary>2024/08/09 Version 0.7.0b1 released</summary>
<p>Simplified installation process, added table recognition functionality</p>
</details>
<details>
<summary>2024/08/01 Version 0.6.2b1 released</summary>
<p>Optimized dependency conflict issues and installation documentation</p>
</details>
<details>
<summary>2024/07/05 Initial open-source release</summary>
</details>
</details>
<!-- TABLE OF CONTENT -->
<details open="open">
<summary><h2 style="display: inline-block">Table of Contents</h2></summary>
<ol>
<li>
<a href="#mineru">MinerU</a>
<ul>
<li><a href="#project-introduction">Project Introduction</a></li>
<li><a href="#key-features">Key Features</a></li>
<li><a href="#quick-start">Quick Start</a>
<ul>
<li><a href="#online-demo">Online Demo</a></li>
<li><a href="#local-deployment">Local Deployment</a></li>
</ul>
</li>
</ul>
</li>
<li><a href="#todo">TODO</a></li>
<li><a href="#known-issues">Known Issues</a></li>
<li><a href="#faq">FAQ</a></li>
<li><a href="#all-thanks-to-our-contributors">All Thanks To Our Contributors</a></li>
<li><a href="#license-information">License Information</a></li>
<li><a href="#acknowledgments">Acknowledgments</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</a></li>
<li><a href="#magic-html">Magic-html</a></li>
<li><a href="#links">Links</a></li>
</ol>
</details>
# 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](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.
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](https://github.com/opendatalab/MinerU/issues) 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 <a href="#faq">FAQ</a>. </br>
If the parsing results are not as expected, refer to the <a href="#known-issues">Known Issues</a>. </br>
There are three different ways to experience MinerU:
- [Online Demo](#online-demo)
- [Local Deployment](#local-deployment)
> [!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.
<table border="1">
<tr>
<td>Parsing Backend</td>
<td>pipeline</td>
<td>vlm-transformers</td>
<td>vlm-sglang</td>
</tr>
<tr>
<td>Operating System</td>
<td>windows/linux/mac</td>
<td>windows/linux</td>
<td>windows(wsl2)/linux</td>
</tr>
<tr>
<td>Memory Requirements</td>
<td colspan="3">Minimum 16GB+, 32GB+ recommended</td>
</tr>
<tr>
<td>Disk Space Requirements</td>
<td colspan="3">20GB+, SSD recommended</td>
</tr>
<tr>
<td>Python Version</td>
<td colspan="3">3.10-3.13</td>
</tr>
<tr>
<td>CPU Inference Support</td>
<td>✅</td>
<td>❌</td>
<td>❌</td>
</tr>
<tr>
<td>GPU Requirements</td>
<td>Turing architecture or later, 6GB+ VRAM or Apple Silicon</td>
<td>Ampere architecture or later, 8GB+ VRAM</td>
<td>Ampere architecture or later, 24GB+ VRAM</td>
</tr>
</table>
## Online Demo
[![OpenDataLab](https://img.shields.io/badge/Demo_on_OpenDataLab-blue?logo=data:image/svg+xml;base64,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&labelColor=white)](https://mineru.net/OpenSourceTools/Extractor?source=github)
[![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)
[![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)
## Local Deployment
### 1. Install MinerU
#### 1.1 Install via pip or uv
```bash
pip install --upgrade pip
pip install uv
uv pip install -U "mineru[core]"
```
#### 1.2 Install from source
```bash
git clone https://github.com/opendatalab/MinerU.git
cd MinerU
uv pip install -e .[core]
```
> [!NOTE]
> Linux and macOS systems automatically support CUDA/MPS acceleration after installation. For Windows users who want to use CUDA acceleration,
> please visit the [PyTorch official website](https://pytorch.org/get-started/locally/) to install PyTorch with the appropriate CUDA version.
#### 1.3 Install Full Version (supports sglang acceleration) (requires device with Ampere or newer architecture and at least 24GB GPU memory)
If you need to use **sglang to accelerate VLM model inference**, you can choose any of the following methods to install the full version:
- Install using uv or pip:
```bash
uv pip install -U "mineru[all]"
```
- Install from source:
```bash
uv pip install -e .[all]
```
- Build image using Dockerfile:
```bash
wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/docker/global/Dockerfile
docker build -t mineru-sglang:latest -f Dockerfile .
```
Start Docker container:
```bash
docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
--ipc=host \
mineru-sglang:latest \
mineru-sglang-server --host 0.0.0.0 --port 30000
```
Or start using Docker Compose:
```bash
wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/docker/compose.yaml
docker compose -f compose.yaml up -d
```
> [!TIP]
> The Dockerfile uses `lmsysorg/sglang:v0.4.7-cu124` as the default base image. If necessary, you can modify it to another platform version.
#### 1.4 Install client (for connecting to sglang-server on edge devices that require only CPU and network connectivity)
```bash
uv pip install -U mineru
mineru -p <input_path> -o <output_path> -b vlm-sglang-client -u http://<host_ip>:<port>
```
---
### 2. Using MinerU
#### 2.1 Command Line Usage
##### Basic Usage
The simplest command line invocation is:
```bash
mineru -p <input_path> -o <output_path>
```
- `<input_path>`: Local PDF file or directory (supports pdf/png/jpg/jpeg)
- `<output_path>`: Output directory
##### View Help Information
Get all available parameter descriptions:
```bash
mineru --help
```
##### Parameter Details
```text
Usage: mineru [OPTIONS]
Options:
-v, --version Show version and exit
-p, --path PATH Input file path or directory (required)
-o, --output PATH Output directory (required)
-m, --method [auto|txt|ocr] Parsing method: auto (default), txt, ocr (pipeline backend only)
-b, --backend [pipeline|vlm-transformers|vlm-sglang-engine|vlm-sglang-client]
Parsing backend (default: pipeline)
-l, --lang [ch|ch_server|... ] Specify document language (improves OCR accuracy, pipeline backend only)
-u, --url TEXT Service address when using sglang-client
-s, --start INTEGER Starting page number (0-based)
-e, --end INTEGER Ending page number (0-based)
-f, --formula BOOLEAN Enable formula parsing (default: on, pipeline backend only)
-t, --table BOOLEAN Enable table parsing (default: on, pipeline backend only)
-d, --device TEXT Inference device (e.g., cpu/cuda/cuda:0/npu/mps, pipeline backend only)
--vram INTEGER Maximum GPU VRAM usage per process (pipeline backend only)
--source [huggingface|modelscope|local]
Model source, default: huggingface
--help Show help information
```
---
#### 2.2 Model Source Configuration
MinerU automatically downloads required models from HuggingFace on first run. If HuggingFace is inaccessible, you can switch model sources:
##### Switch to ModelScope Source
```bash
mineru -p <input_path> -o <output_path> --source modelscope
```
Or set environment variable:
```bash
export MINERU_MODEL_SOURCE=modelscope
mineru -p <input_path> -o <output_path>
```
##### Using Local Models
###### 1. Download Models Locally
```bash
mineru-models-download --help
```
Or use interactive command-line tool to select models:
```bash
mineru-models-download
```
After download, model paths will be displayed in current terminal and automatically written to `mineru.json` in user directory.
###### 2. Parse Using Local Models
```bash
mineru -p <input_path> -o <output_path> --source local
```
Or enable via environment variable:
```bash
export MINERU_MODEL_SOURCE=local
mineru -p <input_path> -o <output_path>
```
---
#### 2.3 Using sglang to Accelerate VLM Model Inference
##### Through the sglang-engine Mode
```bash
mineru -p <input_path> -o <output_path> -b vlm-sglang-engine
```
##### Through the sglang-server/client Mode
1. Start Server:
```bash
mineru-sglang-server --port 30000
```
> [!TIP]
> sglang-server has some commonly used parameters for configuration:
> - If you have two GPUs with `12GB` or `16GB` VRAM, you can use the Tensor Parallel (TP) mode: `--tp 2`
> - If you have two GPUs with `11GB` VRAM, in addition to Tensor Parallel mode, you need to reduce the KV cache size: `--tp 2 --mem-fraction-static 0.7`
> - If you have more than two GPUs with `24GB` VRAM or above, you can use sglang's multi-GPU parallel mode to increase throughput: `--dp 2`
> - You can also enable `torch.compile` to accelerate inference speed by approximately 15%: `--enable-torch-compile`
> - If you want to learn more about the usage of `sglang` parameters, please refer to the [official sglang documentation](https://docs.sglang.ai/backend/server_arguments.html#common-launch-commands)
2. Use Client in another terminal:
```bash
mineru -p <input_path> -o <output_path> -b vlm-sglang-client -u http://127.0.0.1:30000
```
> [!TIP]
> For more information about output files, please refer to [Output File Documentation](docs/output_file_en_us.md)
---
### 3. API Usage
You can also call MinerU through Python code, see example code at:
👉 [Python Usage Example](demo/demo.py)
---
### 4. Deploy Derivative Projects
Community developers have created various extensions based on MinerU, including:
- Graphical interface based on Gradio
- Web API based on FastAPI
- Client/server architecture with multi-GPU load balancing
- MCP Server based on the official API
These projects typically offer better user experience and additional features.
For detailed deployment instructions, please refer to:
👉 [Derivative Projects Documentation](projects/README.md)
---
# TODO
- [x] Reading order based on the model
- [x] Recognition of `index` and `list` in the main text
- [x] Table recognition
- [x] Heading Classification
- [ ] Code block recognition in the main text
- [ ] [Chemical formula recognition](docs/chemical_knowledge_introduction/introduction.pdf)
- [ ] 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](docs/FAQ_zh_cn.md)
[FAQ in English](docs/FAQ_en_us.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)
Currently, 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.
# 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)
- [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)
# Citation
```bibtex
@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
<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)
README_zh-CN.md 0 → 100644
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[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/myhloli/3b3a00a4a0a61577b6c30f989092d20d/mineru_demo.ipynb)
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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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<a href="https://mineru.net/client?source=github">更便捷的使用方式：MinerU桌面端。无需编程，无需登录，图形界面，简单交互，畅用无忧。</a>🚀🚀🚀
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# 更新记录
- 2025/06/20 2.0.6发布
- 修复`vlm`模式下，某些偶发的无效块内容导致解析中断问题
- 修复`vlm`模式下，某些不完整的表结构导致的解析中断问题
- 2025/06/17 2.0.5发布
- 修复了`sglang-client`模式下依然需要下载模型的问题
- 修复了`sglang-client`模式需要依赖`torch`等实际运行不需要的包的问题
- 修复了同一进程内尝试通过多个url启动多个`sglang-client`实例时，只有第一个生效的问题
- 2025/06/15 2.0.3发布
- 修复了当下载模型类型设置为`all`时，配置文件出现键值更新错误的问题
- 修复了命令行模式下公式和表格功能开关不生效导致功能无法关闭的问题
- 修复了`sglang-engine`模式下，0.4.7版本sglang的兼容性问题
- 更新了sglang环境下部署完整版MinerU的Dockerfile和相关安装文档
- 2025/06/13 2.0.0发布
- MinerU 2.0 是一次从架构到功能的全面重构与升级，带来了更简洁的设计、更强的性能以及更灵活的使用体验。
- **全新架构**：MinerU 2.0 在代码结构和交互方式上进行了深度重构，显著提升了系统的易用性、可维护性与扩展能力。
- **去除第三方依赖限制**：彻底移除对 `pymupdf` 的依赖，推动项目向更开放、合规的开源方向迈进。
- **开箱即用，配置便捷**：无需手动编辑 JSON 配置文件，绝大多数参数已支持命令行或 API 直接设置。
- **模型自动管理**：新增模型自动下载与更新机制，用户无需手动干预即可完成模型部署。
- **离线部署友好**：提供内置模型下载命令，支持完全断网环境下的部署需求。
- **代码结构精简**：移除数千行冗余代码，简化类继承逻辑，显著提升代码可读性与开发效率。
- **统一中间格式输出**：采用标准化的 `middle_json` 格式，兼容多数基于该格式的二次开发场景，确保生态业务无缝迁移。
- **全新模型**：MinerU 2.0 集成了我们最新研发的小参数量、高性能多模态文档解析模型，实现端到端的高速、高精度文档理解。
- **小模型，大能力**：模型参数不足 1B，却在解析精度上超越传统 72B 级别的视觉语言模型（VLM）。
- **多功能合一**：单模型覆盖多语言识别、手写识别、版面分析、表格解析、公式识别、阅读顺序排序等核心任务。
- **极致推理速度**：在单卡 NVIDIA 4090 上通过 `sglang` 加速，达到峰值吞吐量超过 10,000 token/s，轻松应对大规模文档处理需求。
- **在线体验**：您可以在[MinerU.net](https://mineru.net/OpenSourceTools/Extractor)、[Hugging Face](https://huggingface.co/spaces/opendatalab/MinerU), 以及[ModelScope](https://www.modelscope.cn/studios/OpenDataLab/MinerU)体验我们的全新VLM模型
- **不兼容变更说明**:为提升整体架构合理性与长期可维护性，本版本包含部分不兼容的变更：
- Python 包名从 `magic-pdf` 更改为 `mineru`，命令行工具也由 `magic-pdf` 改为 `mineru`，请同步更新脚本与调用命令。
- 出于对系统模块化设计与生态一致性的考虑，MinerU 2.0 已不再内置 LibreOffice 文档转换模块。如需处理 Office 文档，建议通过独立部署的 LibreOffice 服务先行转换为 PDF 格式，再进行后续解析操作。
<details>
<summary>历史日志</summary>
<details>
<summary>2025/05/24 1.3.12 发布</summary>
<ul>
<li>增加ppocrv5模型的支持，将<code>ch_server</code>模型更新为<code>PP-OCRv5_rec_server</code>，<code>ch_lite</code>模型更新为<code>PP-OCRv5_rec_mobile</code>（需更新模型）
<ul>
<li>在测试中，发现ppocrv5(server)对手写文档效果有一定提升，但在其余类别文档的精度略差于v4_server_doc，因此默认的ch模型保持不变，仍为<code>PP-OCRv4_server_rec_doc</code>。</li>
<li>由于ppocrv5强化了手写场景和特殊字符的识别能力，因此您可以在日繁混合场景以及手写文档场景下手动选择使用ppocrv5模型</li>
<li>您可通过lang参数<code>lang='ch_server'</code>(python api)或<code>--lang ch_server</code>(命令行)自行选择相应的模型：
<ul>
<li><code>ch</code> ：<code>PP-OCRv4_rec_server_doc</code>（默认）（中英日繁混合/1.5w字典）</li>
<li><code>ch_server</code> ：<code>PP-OCRv5_rec_server</code>（中英日繁混合+手写场景/1.8w字典）</li>
<li><code>ch_lite</code> ：<code>PP-OCRv5_rec_mobile</code>（中英日繁混合+手写场景/1.8w字典）</li>
<li><code>ch_server_v4</code> ：<code>PP-OCRv4_rec_server</code>（中英混合/6k字典）</li>
<li><code>ch_lite_v4</code> ：<code>PP-OCRv4_rec_mobile</code>（中英混合/6k字典）</li>
</ul>
</li>
</ul>
</li>
<li>增加手写文档的支持，通过优化layout对手写文本区域的识别，现已支持手写文档的解析
<ul>
<li>默认支持此功能，无需额外配置</li>
<li>可以参考上述说明，手动选择ppocrv5模型以获得更好的手写文档解析效果</li>
</ul>
</li>
<li><code>huggingface</code>和<code>modelscope</code>的demo已更新为支持手写识别和ppocrv5模型的版本，可自行在线体验</li>
</ul>
</details>
<details>
<summary>2025/04/29 1.3.10 发布</summary>
<ul>
<li>支持使用自定义公式标识符，可通过修改用户目录下的<code>magic-pdf.json</code>文件中的<code>latex-delimiter-config</code>项实现。</li>
</ul>
</details>
<details>
<summary>2025/04/27 1.3.9 发布</summary>
<ul>
<li>优化公式解析功能，提升公式渲染的成功率</li>
</ul>
</details>
<details>
<summary>2025/04/23 1.3.8 发布</summary>
<ul>
<li><code>ocr</code>默认模型(<code>ch</code>)更新为<code>PP-OCRv4_server_rec_doc</code>（需更新模型）
<ul>
<li><code>PP-OCRv4_server_rec_doc</code>是在<code>PP-OCRv4_server_rec</code>的基础上，在更多中文文档数据和PP-OCR训练数据的混合数据训练而成，增加了部分繁体字、日文、特殊字符的识别能力，可支持识别的字符为1.5万+，除文档相关的文字识别能力提升外，也同时提升了通用文字的识别能力。</li>
<li><a href="https://paddlepaddle.github.io/PaddleX/latest/module_usage/tutorials/ocr_modules/text_recognition.html#_3">PP-OCRv4_server_rec_doc/PP-OCRv4_server_rec/PP-OCRv4_mobile_rec 性能对比</a></li>
<li>经验证，<code>PP-OCRv4_server_rec_doc</code>模型在<code>中英日繁</code>单种语言或多种语言混合场景均有明显精度提升，且速度与<code>PP-OCRv4_server_rec</code>相当，适合绝大部分场景使用。</li>
<li><code>PP-OCRv4_server_rec_doc</code>在小部分纯英文场景可能会发生单词粘连问题，<code>PP-OCRv4_server_rec</code>则在此场景下表现更好，因此我们保留了<code>PP-OCRv4_server_rec</code>模型，用户可通过增加参数<code>lang='ch_server'</code>(python api)或<code>--lang ch_server</code>(命令行)调用。</li>
</ul>
</li>
</ul>
</details>
<details>
<summary>2025/04/22 1.3.7 发布</summary>
<ul>
<li>修复表格解析模型初始化时lang参数失效的问题</li>
<li>修复在<code>cpu</code>模式下ocr和表格解析速度大幅下降的问题</li>
</ul>
</details>
<details>
<summary>2025/04/16 1.3.4 发布</summary>
<ul>
<li>通过移除一些无用的块，小幅提升了ocr-det的速度</li>
<li>修复部分情况下由footnote导致的页面内排序错误</li>
</ul>
</details>
<details>
<summary>2025/04/12 1.3.2 发布</summary>
<ul>
<li>修复了windows系统下，在python3.13环境安装时一些依赖包版本不兼容的问题</li>
<li>优化批量推理时的内存占用</li>
<li>优化旋转90度表格的解析效果</li>
<li>优化财报样本中超大表格的解析效果</li>
<li>修复了在未指定OCR语言时，英文文本区域偶尔出现的单词黏连问题（需要更新模型）</li>
</ul>
</details>
<details>
<summary>2025/04/08 1.3.1 发布</summary>
<ul>
<li>修复了一些兼容问题
<ul>
<li>支持python 3.13</li>
<li>为部分过时的linux系统（如centos7）做出最后适配，并不再保证后续版本的继续支持，<a href="https://github.com/opendatalab/MinerU/issues/1004">安装说明</a></li>
</ul>
</li>
</ul>
</details>
<details>
<summary>2025/04/03 1.3.0 发布</summary>
<ul>
<li>安装与兼容性优化
<ul>
<li>通过移除layout中<code>layoutlmv3</code>的使用，解决了由<code>detectron2</code>导致的兼容问题</li>
<li>torch版本兼容扩展到2.2~2.6(2.5除外)</li>
<li>cuda兼容支持11.8/12.4/12.6/12.8（cuda版本由torch决定），解决部分用户50系显卡与H系显卡的兼容问题</li>
<li>python兼容版本扩展到3.10~3.12，解决了在非3.10环境下安装时自动降级到0.6.1的问题</li>
<li>优化离线部署流程，部署成功后不需要联网下载任何模型文件</li>
</ul>
</li>
<li>性能优化
<ul>
<li>通过支持多个pdf文件的batch处理（<a href="demo/batch_demo.py">脚本样例</a>），提升了批量小文件的解析速度 (与1.0.1版本相比，公式解析速度最高提升超过1400%，整体解析速度最高提升超过500%)</li>
<li>通过优化mfr模型的加载和使用，降低了显存占用并提升了解析速度(需重新执行<a href="docs/how_to_download_models_zh_cn.md">模型下载流程</a>以获得模型文件的增量更新)</li>
<li>优化显存占用，最低仅需6GB即可运行本项目</li>
<li>优化了在mps设备上的运行速度</li>
</ul>
</li>
<li>解析效果优化
<ul>
<li>mfr模型更新到<code>unimernet(2503)</code>，解决多行公式中换行丢失的问题</li>
</ul>
</li>
<li>易用性优化
<ul>
<li>通过使用<code>paddleocr2torch</code>，完全替代<code>paddle</code>框架以及<code>paddleocr</code>在项目中的使用，解决了<code>paddle</code>和<code>torch</code>的冲突问题，和由于<code>paddle</code>框架导致的线程不安全问题</li>
<li>解析过程增加实时进度条显示，精准把握解析进度，让等待不再痛苦</li>
</ul>
</li>
</ul>
</details>
<details>
<summary>2025/03/03 1.2.1 发布，修复了一些问题</summary>
<ul>
<li>修复在字母与数字的全角转半角操作时对标点符号的影响</li>
<li>修复在某些情况下caption的匹配不准确问题</li>
<li>修复在某些情况下的公式span丢失问题</li>
</ul>
</details>
<details>
<summary>2025/02/24 1.2.0 发布，这个版本我们修复了一些问题，提升了解析的效率与精度：</summary>
<ul>
<li>性能优化
<ul>
<li>auto模式下pdf文档的分类速度提升</li>
</ul>
</li>
<li>解析优化
<ul>
<li>优化对包含水印文档的解析逻辑，显著提升包含水印文档的解析效果</li>
<li>改进了单页内多个图像/表格与caption的匹配逻辑，提升了复杂布局下图文匹配的准确性</li>
</ul>
</li>
<li>问题修复
<ul>
<li>修复在某些情况下图片/表格span被填充进textblock导致的异常</li>
<li>修复在某些情况下标题block为空的问题</li>
</ul>
</li>
</ul>
</details>
<details>
<summary>2025/01/22 1.1.0 发布，在这个版本我们重点提升了解析的精度与效率：</summary>
<ul>
<li>模型能力升级（需重新执行 <a href="https://github.com/opendatalab/MinerU/docs/how_to_download_models_zh_cn.md">模型下载流程</a> 以获得模型文件的增量更新）
<ul>
<li>布局识别模型升级到最新的 `doclayout_yolo(2501)` 模型，提升了layout识别精度</li>
<li>公式解析模型升级到最新的 `unimernet(2501)` 模型，提升了公式识别精度</li>
</ul>
</li>
<li>性能优化
<ul>
<li>在配置满足一定条件（显存16GB+）的设备上，通过优化资源占用和重构处理流水线，整体解析速度提升50%以上</li>
</ul>
</li>
<li>解析效果优化
<ul>
<li>在线demo（<a href="https://mineru.net/OpenSourceTools/Extractor">mineru.net</a> / <a href="https://huggingface.co/spaces/opendatalab/MinerU">huggingface</a> / <a href="https://www.modelscope.cn/studios/OpenDataLab/MinerU">modelscope</a>）上新增标题分级功能（测试版本，默认开启），支持对标题进行分级，提升文档结构化程度</li>
</ul>
</li>
</ul>
</details>
<details>
<summary>2025/01/10 1.0.1 发布，这是我们的第一个正式版本，在这个版本中，我们通过大量重构带来了全新的API接口和更广泛的兼容性，以及全新的自动语言识别功能：</summary>
<ul>
<li>全新API接口
<ul>
<li>对于数据侧API，我们引入了Dataset类，旨在提供一个强大而灵活的数据处理框架。该框架当前支持包括图像（.jpg及.png）、PDF、Word（.doc及.docx）、以及PowerPoint（.ppt及.pptx）在内的多种文档格式，确保了从简单到复杂的数据处理任务都能得到有效的支持。</li>
<li>针对用户侧API，我们将MinerU的处理流程精心设计为一系列可组合的Stage阶段。每个Stage代表了一个特定的处理步骤，用户可以根据自身需求自由地定义新的Stage，并通过创造性地组合这些阶段来定制专属的数据处理流程。</li>
</ul>
</li>
<li>更广泛的兼容性适配
<ul>
<li>通过优化依赖环境和配置项，确保在ARM架构的Linux系统上能够稳定高效运行。</li>
<li>深度适配华为昇腾NPU加速，积极响应信创要求，提供自主可控的高性能计算能力，助力人工智能应用平台的国产化应用与发展。 <a href="https://github.com/opendatalab/MinerU/docs/README_Ascend_NPU_Acceleration_zh_CN.md">NPU加速教程</a></li>
</ul>
</li>
<li>自动语言识别
<ul>
<li>通过引入全新的语言识别模型， 在文档解析中将 `lang` 配置为 `auto`，即可自动选择合适的OCR语言模型，提升扫描类文档解析的准确性。</li>
</ul>
</li>
</ul>
</details>
<details>
<summary>2024/11/22 0.10.0发布，通过引入混合OCR文本提取能力，</summary>
<ul>
<li>在公式密集、span区域不规范、部分文本使用图像表现等复杂文本分布场景下获得解析效果的显著提升</li>
<li>同时具备文本模式内容提取准确、速度更快与OCR模式span/line区域识别更准的双重优势</li>
</ul>
</details>
<details>
<summary>2024/11/15 0.9.3发布，为表格识别功能接入了<a href="https://github.com/RapidAI/RapidTable">RapidTable</a>,单表解析速度提升10倍以上，准确率更高，显存占用更低</summary>
</details>
<details>
<summary>2024/11/06 0.9.2发布，为表格识别功能接入了<a href="https://huggingface.co/U4R/StructTable-InternVL2-1B">StructTable-InternVL2-1B</a>模型</summary>
</details>
<details>
<summary>2024/10/31 0.9.0发布，这是我们进行了大量代码重构的全新版本，解决了众多问题，提升了性能，降低了硬件需求，并提供了更丰富的易用性：</summary>
<ul>
<li>重构排序模块代码，使用 <a href="https://github.com/ppaanngggg/layoutreader">layoutreader</a> 进行阅读顺序排序，确保在各种排版下都能实现极高准确率</li>
<li>重构段落拼接模块，在跨栏、跨页、跨图、跨表情况下均能实现良好的段落拼接效果</li>
<li>重构列表和目录识别功能，极大提升列表块和目录块识别的准确率及对应文本段落的解析效果</li>
<li>重构图、表与描述性文本的匹配逻辑，大幅提升 caption 和 footnote 与图表的匹配准确率，并将描述性文本的丢失率降至接近0</li>
<li>增加 OCR 的多语言支持，支持 84 种语言的检测与识别，语言支持列表详见 <a href="https://paddlepaddle.github.io/PaddleOCR/latest/ppocr/blog/multi_languages.html#5">OCR 语言支持列表</a></li>
<li>增加显存回收逻辑及其他显存优化措施，大幅降低显存使用需求。开启除表格加速外的全部加速功能(layout/公式/OCR)的显存需求从16GB降至8GB，开启全部加速功能的显存需求从24GB降至10GB</li>
<li>优化配置文件的功能开关，增加独立的公式检测开关，无需公式检测时可大幅提升速度和解析效果</li>
<li>集成 <a href="https://github.com/opendatalab/PDF-Extract-Kit">PDF-Extract-Kit 1.0</a>
<ul>
<li>加入自研的 `doclayout_yolo` 模型，在相近解析效果情况下比原方案提速10倍以上，可通过配置文件与 `layoutlmv3` 自由切换</li>
<li>公式解析升级至 `unimernet 0.2.1`，在提升公式解析准确率的同时，大幅降低显存需求</li>
<li>因 `PDF-Extract-Kit 1.0` 更换仓库，需要重新下载模型，步骤详见 <a href="https://github.com/opendatalab/MinerU/docs/how_to_download_models_zh_cn.md">如何下载模型</a></li>
</ul>
</li>
</ul>
</details>
<details>
<summary>2024/09/27 0.8.1发布，修复了一些bug，同时提供了<a href="https://opendatalab.com/OpenSourceTools/Extractor/PDF/">在线demo</a>的<a href="https://github.com/opendatalab/MinerU/projects/web_demo/README_zh-CN.md">本地化部署版本</a>和<a href="https://github.com/opendatalab/MinerU/projects/web/README_zh-CN.md">前端界面</a></summary>
</details>
<details>
<summary>2024/09/09 0.8.0发布，支持Dockerfile快速部署，同时上线了huggingface、modelscope demo</summary>
</details>
<details>
<summary>2024/08/30 0.7.1发布，集成了paddle tablemaster表格识别功能</summary>
</details>
<details>
<summary>2024/08/09 0.7.0b1发布，简化安装步骤提升易用性，加入表格识别功能</summary>
</details>
<details>
<summary>2024/08/01 0.6.2b1发布，优化了依赖冲突问题和安装文档</summary>
</details>
<details>
<summary>2024/07/05 首次开源</summary>
</details>
</details>
<!-- 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="#本地部署">本地部署</a></li>
</ul>
</ul>
</li>
<li><a href="#todo">TODO</a></li>
<li><a href="#known-issues">Known Issues</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，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84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-issues">Known Issues</a></br>
有2种不同方式可以体验MinerU的效果：
- [在线体验](#在线体验)
- [本地部署](#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。
<table border="1">
<tr>
<td>解析后端</td>
<td>pipeline</td>
<td>vlm-transformers</td>
<td>vlm-sglang</td>
</tr>
<tr>
<td>操作系统</td>
<td>windows/linux/mac</td>
<td>windows/linux</td>
<td>windows(wsl2)/linux</td>
</tr>
<tr>
<td>内存要求</td>
<td colspan="3">最低16G以上，推荐32G以上</td>
</tr>
<tr>
<td>磁盘空间要求</td>
<td colspan="3">20G以上，推荐使用SSD</td>
</tr>
<tr>
<td>python版本</td>
<td colspan="3">3.10-3.13</td>
</tr>
<tr>
<td>CPU推理支持</td>
<td>✅</td>
<td>❌</td>
<td>❌</td>
</tr>
<tr>
<td>GPU要求</td>
<td>Turing及以后架构，6G显存以上或Apple Silicon</td>
<td>Ampere及以后架构，8G显存以上</td>
<td>Ampere及以后架构，24G显存及以上</td>
</tr>
</table>
## 在线体验
[![OpenDataLab](https://img.shields.io/badge/Demo_on_OpenDataLab-blue?logo=data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTM0IiBoZWlnaHQ9IjEzNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj48cGF0aCBkPSJtMTIyLDljMCw1LTQsOS05LDlzLTktNC05LTksNC05LDktOSw5LDQsOSw5eiIgZmlsbD0idXJsKCNhKSIvPjxwYXRoIGQ9Im0xMjIsOWMwLDUtNCw5LTksOXMtOS00LTktOSw0LTksOS05LDksNCw5LDl6IiBmaWxsPSIjMDEwMTAxIi8+PHBhdGggZD0ibTkxLDE4YzAsNS00LDktOSw5cy05LTQtOS05LDQtOSw5LTksOSw0LDksOXoiIGZpbGw9InVybCgjYikiLz48cGF0aCBkPSJtOTEsMThjMCw1LTQsOS05LDlzLTktNC05LTksNC05LDktOSw5LDQsOSw5eiIgZmlsbD0iIzAxMDEwMSIvPjxwYXRoIGZpbGwtcnVsZT0iZXZlbm9kZCIgY2xpcC1ydWxlPSJldmVub2RkIiBkPSJtMzksNjJjMCwxNiw4LDMwLDIwLDM4LDctNiwxMi0xNiwxMi0yNlY0OWMwLTQsMy03LDYtOGw0Ni0xMmM1LTEsMTEsMywxMSw4djMxYzAsMzctMzAsNjYtNjYsNjYtMzcsMC02Ni0zMC02Ni02NlY0NmMwLTQsMy03LDYtOGwyMC02YzUtMSwxMSwzLDExLDh2MjF6bS0yOSw2YzAsMTYsNiwzMCwxNyw0MCwzLDEsNSwxLDgsMSw1LDAsMTAtMSwxNS0zQzM3LDk1LDI5LDc5LDI5LDYyVjQybC0xOSw1djIweiIgZmlsbD0idXJsKCNjKSIvPjxwYXRoIGZpbGwtcnVsZT0iZXZlbm9kZCIgY2xpcC1ydWxlPSJldmVub2RkIiBkPSJtMzksNjJjMCwxNiw4LDMwLDIwLDM4LDctNiwxMi0xNiwxMi0yNlY0OWMwLTQsMy03LDYtOGw0Ni0xMmM1LTEsMTEsMywxMSw4djMxYzAsMzctMzAsNjYtNjYsNjYtMzcsMC02Ni0zMC02Ni02NlY0NmMwLTQsMy03LDYtOGwyMC02YzUtMSwxMSwzLDExLDh2MjF6bS0yOSw2YzAsMTYsNiwzMCwxNyw0MCwzLDEsNSwxLDgsMSw1LDAsMTAtMSwxNS0zQzM3LDk1LDI5LDc5LDI5LDYyVjQybC0xOSw1djIweiIgZmlsbD0iIzAxMDEwMSIvPjxkZWZzPjxsaW5lYXJHcmFkaWVudCBpZD0iYSIgeDE9Ijg0IiB5MT0iNDEiIHgyPSI3NSIgeTI9IjEyMCIgZ3JhZGllbnRVbml0cz0idXNlclNwYWNlT25Vc2UiPjxzdG9wIHN0b3AtY29sb3I9IiNmZmYiLz48c3RvcCBvZmZzZXQ9IjEiIHN0b3AtY29sb3I9IiMyZTJlMmUiLz48L2xpbmVhckdyYWRpZW50PjxsaW5lYXJHcmFkaWVudCBpZD0iYiIgeDE9Ijg0IiB5MT0iNDEiIHgyPSI3NSIgeTI9IjEyMCIgZ3JhZGllbnRVbml0cz0idXNlclNwYWNlT25Vc2UiPjxzdG9wIHN0b3AtY29sb3I9IiNmZmYiLz48c3RvcCBvZmZzZXQ9IjEiIHN0b3AtY29sb3I9IiMyZTJlMmUiLz48L2xpbmVhckdyYWRpZW50PjxsaW5lYXJHcmFkaWVudCBpZD0iYyIgeDE9Ijg0IiB5MT0iNDEiIHgyPSI3NSIgeTI9IjEyMCIgZ3JhZGllbnRVbml0cz0idXNlclNwYWNlT25Vc2UiPjxzdG9wIHN0b3AtY29sb3I9IiNmZmYiLz48c3RvcCBvZmZzZXQ9IjEiIHN0b3AtY29sb3I9IiMyZTJlMmUiLz48L2xpbmVhckdyYWRpZW50PjwvZGVmcz48L3N2Zz4=&labelColor=white)](https://mineru.net/OpenSourceTools/Extractor?source=github)
[![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
### 1. 安装 MinerU
#### 1.1 使用 pip 或 uv 安装
```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[core]" -i https://mirrors.aliyun.com/pypi/simple
```
#### 1.2 源码安装
```bash
git clone https://github.com/opendatalab/MinerU.git
cd MinerU
uv pip install -e .[core] -i https://mirrors.aliyun.com/pypi/simple
```
> [!NOTE]
> Linux和macOS系统安装后自动支持cuda/mps加速，Windows用户如需使用cuda加速，
> 请前往 [Pytorch官网](https://pytorch.org/get-started/locally/) 选择合适的cuda版本安装pytorch。
#### 1.3 安装完整版（支持 sglang 加速）（需确保设备有Ampere及以后架构，24G显存及以上显卡）
如需使用 **sglang 加速 VLM 模型推理**，请选择合适的方式安装完整版本：
- 使用uv或pip安装
```bash
uv pip install -U "mineru[all]" -i https://mirrors.aliyun.com/pypi/simple
```
- 从源码安装：
```bash
uv pip install -e .[all] -i https://mirrors.aliyun.com/pypi/simple
```
- 使用 Dockerfile 构建镜像：
```bash
wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/docker/china/Dockerfile
docker build -t mineru-sglang:latest -f Dockerfile .
```
启动 Docker 容器：
```bash
docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
--ipc=host \
mineru-sglang:latest \
mineru-sglang-server --host 0.0.0.0 --port 30000
```
或使用 Docker Compose 启动：
```bash
wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/docker/compose.yaml
docker compose -f compose.yaml up -d
```
> [!TIP]
> Dockerfile默认使用`lmsysorg/sglang:v0.4.7-cu124`作为基础镜像，如有需要，您可以自行修改为其他平台版本。
#### 1.4 安装client（用于在仅需 CPU 和网络连接的边缘设备上连接 sglang-server）
```bash
uv pip install -U mineru -i https://mirrors.aliyun.com/pypi/simple
mineru -p <input_path> -o <output_path> -b vlm-sglang-client -u http://<host_ip>:<port>
```
---
### 2. 使用 MinerU
#### 2.1 命令行使用方式
##### 基础用法
最简单的命令行调用方式如下：
```bash
mineru -p <input_path> -o <output_path>
```
- `<input_path>`：本地 PDF 文件或目录（支持 pdf/png/jpg/jpeg）
- `<output_path>`：输出目录
##### 查看帮助信息
获取所有可用参数说明：
```bash
mineru --help
```
##### 参数详解
```text
Usage: mineru [OPTIONS]
Options:
-v, --version 显示版本并退出
-p, --path PATH 输入文件路径或目录（必填）
-o, --output PATH 输出目录（必填）
-m, --method [auto|txt|ocr] 解析方法：auto（默认）、txt、ocr（仅用于 pipeline 后端）
-b, --backend [pipeline|vlm-transformers|vlm-sglang-engine|vlm-sglang-client]
解析后端（默认为 pipeline）
-l, --lang [ch|ch_server|... ] 指定文档语言（可提升 OCR 准确率，仅用于 pipeline 后端）
-u, --url TEXT 当使用 sglang-client 时，需指定服务地址
-s, --start INTEGER 开始解析的页码（从 0 开始）
-e, --end INTEGER 结束解析的页码（从 0 开始）
-f, --formula BOOLEAN 是否启用公式解析（默认开启，仅 pipeline 后端）
-t, --table BOOLEAN 是否启用表格解析（默认开启，仅 pipeline 后端）
-d, --device TEXT 推理设备（如 cpu/cuda/cuda:0/npu/mps，仅 pipeline 后端）
--vram INTEGER 单进程最大 GPU 显存占用（仅 pipeline 后端）
--source [huggingface|modelscope|local]
模型来源，默认 huggingface
--help 显示帮助信息
```
---
#### 2.2 模型源配置
MinerU 默认在首次运行时自动从 HuggingFace 下载所需模型。若无法访问 HuggingFace，可通过以下方式切换模型源：
##### 切换至 ModelScope 源
```bash
mineru -p <input_path> -o <output_path> --source modelscope
```
或设置环境变量：
```bash
export MINERU_MODEL_SOURCE=modelscope
mineru -p <input_path> -o <output_path>
```
##### 使用本地模型
###### 1. 下载模型到本地
```bash
mineru-models-download --help
```
或使用交互式命令行工具选择模型下载：
```bash
mineru-models-download
```
下载完成后，模型路径会在当前终端窗口输出，并自动写入用户目录下的 `mineru.json`。
###### 2. 使用本地模型进行解析
```bash
mineru -p <input_path> -o <output_path> --source local
```
或通过环境变量启用：
```bash
export MINERU_MODEL_SOURCE=local
mineru -p <input_path> -o <output_path>
```
---
#### 2.3 使用 sglang 加速 VLM 模型推理
##### 通过 sglang-engine 模式
```bash
mineru -p <input_path> -o <output_path> -b vlm-sglang-engine
```
##### 通过 sglang-server/client 模式
1. 启动 Server：
```bash
mineru-sglang-server --port 30000
```
> [!TIP]
> sglang-server 有一些常用参数可以配置：
> - 如您有两张显存为`12G`或`16G`的显卡，可以通过张量并行（TP）模式使用：`--tp 2`
> - 如您有两张`11G`显卡，除了张量并行外，还需要调低KV缓存大小，可以使用：`--tp 2 --mem-fraction-static 0.7`
> - 如果您有超过多张`24G`以上显卡，可以使用sglang的多卡并行模式来增加吞吐量：`--dp 2`
> - 同时您可以启用`torch.compile`来将推理速度加速约15%：`--enable-torch-compile`
> - 如果您想了解更多有关`sglang`的参数使用方法，请参考 [sglang官方文档](https://docs.sglang.ai/backend/server_arguments.html#common-launch-commands)
2. 在另一个终端中使用 Client 调用：
```bash
mineru -p <input_path> -o <output_path> -b vlm-sglang-client -u http://127.0.0.1:30000
```
> [!TIP]
> 更多关于输出文件的信息，请参考 [输出文件说明](docs/output_file_zh_cn.md)
---
### 3. API 调用方式
您也可以通过 Python 代码调用 MinerU，示例代码请参考：
👉 [Python 调用示例](demo/demo.py)
---
### 4. 部署衍生项目
社区开发者基于 MinerU 进行了多种二次开发，包括：
- 基于 Gradio 的图形界面
- 基于 FastAPI 的 Web API
- 多卡负载均衡的客户端/服务端架构
- 基于官网API的MCP Server
这些项目通常提供更好的用户体验和更多功能。
详细部署方式请参阅：
👉 [衍生项目说明](projects/README_zh-CN.md)
---
# 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
- [ ] 正文中代码块识别
- [ ] [化学式识别](docs/chemical_knowledge_introduction/introduction.pdf)
- [ ] 几何图形识别
# 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
- 不支持竖排文字
- 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（如拉丁文的重音符号、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
[常见问题](docs/FAQ_zh_cn.md)
[FAQ](docs/FAQ_en_us.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)
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)
- [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)
# Citation
```bibtex
@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
<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)
SECURITY.md 0 → 100644
View file @ 0fc1daac
# Security Policy
## Supported Versions
latest
## Reporting a Vulnerability
Please do not report security vulnerabilities through public GitHub issues.
Instead, please report them at https://github.com/opendatalab/MinerU/security.
Please include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue:
* Type of issue (e.g. buffer overflow, SQL injection, cross-site scripting, etc.)
* Full paths of source file(s) related to the manifestation of the issue
* The location of the affected source code (tag/branch/commit or direct URL)
* Any special configuration required to reproduce the issue
* Step-by-step instructions to reproduce the issue
* Proof-of-concept or exploit code (if possible)
* Impact of the issue, including how an attacker might exploit the issue
This information will help us triage your report more quickly.
## Preferred Languages
We prefer all communications to be in English and Chinese.
## Policy
We will fix security issues in the project's own code as quickly as possible. Before the project completes the fix, you must not disclose the vulnerability information to any public platform.
demo/demo.py 0 → 100644
View file @ 0fc1daac
# Copyright (c) Opendatalab. All rights reserved.
import copy
import json
import os
from pathlib import Path
from loguru import logger
from mineru.cli.common import convert_pdf_bytes_to_bytes_by_pypdfium2, prepare_env, read_fn
from mineru.data.data_reader_writer import FileBasedDataWriter
from mineru.utils.draw_bbox import draw_layout_bbox, draw_span_bbox
from mineru.utils.enum_class import MakeMode
from mineru.backend.vlm.vlm_analyze import doc_analyze as vlm_doc_analyze
from mineru.backend.pipeline.pipeline_analyze import doc_analyze as pipeline_doc_analyze
from mineru.backend.pipeline.pipeline_middle_json_mkcontent import union_make as pipeline_union_make
from mineru.backend.pipeline.model_json_to_middle_json import result_to_middle_json as pipeline_result_to_middle_json
from mineru.backend.vlm.vlm_middle_json_mkcontent import union_make as vlm_union_make
from mineru.utils.models_download_utils import auto_download_and_get_model_root_path
def do_parse(
output_dir, # Output directory for storing parsing results
pdf_file_names: list[str], # List of PDF file names to be parsed
pdf_bytes_list: list[bytes], # List of PDF bytes to be parsed
p_lang_list: list[str], # List of languages for each PDF, default is 'ch' (Chinese)
backend="pipeline", # The backend for parsing PDF, default is 'pipeline'
parse_method="auto", # The method for parsing PDF, default is 'auto'
p_formula_enable=True, # Enable formula parsing
p_table_enable=True, # Enable table parsing
server_url=None, # Server URL for vlm-sglang-client backend
f_draw_layout_bbox=True, # Whether to draw layout bounding boxes
f_draw_span_bbox=True, # Whether to draw span bounding boxes
f_dump_md=True, # Whether to dump markdown files
f_dump_middle_json=True, # Whether to dump middle JSON files
f_dump_model_output=True, # Whether to dump model output files
f_dump_orig_pdf=True, # Whether to dump original PDF files
f_dump_content_list=True, # Whether to dump content list files
f_make_md_mode=MakeMode.MM_MD, # The mode for making markdown content, default is MM_MD
start_page_id=0, # Start page ID for parsing, default is 0
end_page_id=None, # End page ID for parsing, default is None (parse all pages until the end of the document)
):
if backend == "pipeline":
for idx, pdf_bytes in enumerate(pdf_bytes_list):
new_pdf_bytes = convert_pdf_bytes_to_bytes_by_pypdfium2(pdf_bytes, start_page_id, end_page_id)
pdf_bytes_list[idx] = new_pdf_bytes
infer_results, all_image_lists, all_pdf_docs, lang_list, ocr_enabled_list = pipeline_doc_analyze(pdf_bytes_list, p_lang_list, parse_method=parse_method, formula_enable=p_formula_enable,table_enable=p_table_enable)
for idx, model_list in enumerate(infer_results):
model_json = copy.deepcopy(model_list)
pdf_file_name = pdf_file_names[idx]
local_image_dir, local_md_dir = prepare_env(output_dir, pdf_file_name, parse_method)
image_writer, md_writer = FileBasedDataWriter(local_image_dir), FileBasedDataWriter(local_md_dir)
images_list = all_image_lists[idx]
pdf_doc = all_pdf_docs[idx]
_lang = lang_list[idx]
_ocr_enable = ocr_enabled_list[idx]
middle_json = pipeline_result_to_middle_json(model_list, images_list, pdf_doc, image_writer, _lang, _ocr_enable, p_formula_enable)
pdf_info = middle_json["pdf_info"]
pdf_bytes = pdf_bytes_list[idx]
if f_draw_layout_bbox:
draw_layout_bbox(pdf_info, pdf_bytes, local_md_dir, f"{pdf_file_name}_layout.pdf")
if f_draw_span_bbox:
draw_span_bbox(pdf_info, pdf_bytes, local_md_dir, f"{pdf_file_name}_span.pdf")
if f_dump_orig_pdf:
md_writer.write(
f"{pdf_file_name}_origin.pdf",
pdf_bytes,
)
if f_dump_md:
image_dir = str(os.path.basename(local_image_dir))
md_content_str = pipeline_union_make(pdf_info, f_make_md_mode, image_dir)
md_writer.write_string(
f"{pdf_file_name}.md",
md_content_str,
)
if f_dump_content_list:
image_dir = str(os.path.basename(local_image_dir))
content_list = pipeline_union_make(pdf_info, MakeMode.CONTENT_LIST, image_dir)
md_writer.write_string(
f"{pdf_file_name}_content_list.json",
json.dumps(content_list, ensure_ascii=False, indent=4),
)
if f_dump_middle_json:
md_writer.write_string(
f"{pdf_file_name}_middle.json",
json.dumps(middle_json, ensure_ascii=False, indent=4),
)
if f_dump_model_output:
md_writer.write_string(
f"{pdf_file_name}_model.json",
json.dumps(model_json, ensure_ascii=False, indent=4),
)
logger.info(f"local output dir is {local_md_dir}")
else:
if backend.startswith("vlm-"):
backend = backend[4:]
f_draw_span_bbox = False
parse_method = "vlm"
for idx, pdf_bytes in enumerate(pdf_bytes_list):
pdf_file_name = pdf_file_names[idx]
pdf_bytes = convert_pdf_bytes_to_bytes_by_pypdfium2(pdf_bytes, start_page_id, end_page_id)
local_image_dir, local_md_dir = prepare_env(output_dir, pdf_file_name, parse_method)
image_writer, md_writer = FileBasedDataWriter(local_image_dir), FileBasedDataWriter(local_md_dir)
middle_json, infer_result = vlm_doc_analyze(pdf_bytes, image_writer=image_writer, backend=backend, server_url=server_url)
pdf_info = middle_json["pdf_info"]
if f_draw_layout_bbox:
draw_layout_bbox(pdf_info, pdf_bytes, local_md_dir, f"{pdf_file_name}_layout.pdf")
if f_draw_span_bbox:
draw_span_bbox(pdf_info, pdf_bytes, local_md_dir, f"{pdf_file_name}_span.pdf")
if f_dump_orig_pdf:
md_writer.write(
f"{pdf_file_name}_origin.pdf",
pdf_bytes,
)
if f_dump_md:
image_dir = str(os.path.basename(local_image_dir))
md_content_str = vlm_union_make(pdf_info, f_make_md_mode, image_dir)
md_writer.write_string(
f"{pdf_file_name}.md",
md_content_str,
)
if f_dump_content_list:
image_dir = str(os.path.basename(local_image_dir))
content_list = vlm_union_make(pdf_info, MakeMode.CONTENT_LIST, image_dir)
md_writer.write_string(
f"{pdf_file_name}_content_list.json",
json.dumps(content_list, ensure_ascii=False, indent=4),
)
if f_dump_middle_json:
md_writer.write_string(
f"{pdf_file_name}_middle.json",
json.dumps(middle_json, ensure_ascii=False, indent=4),
)
if f_dump_model_output:
model_output = ("\n" + "-" * 50 + "\n").join(infer_result)
md_writer.write_string(
f"{pdf_file_name}_model_output.txt",
model_output,
)
logger.info(f"local output dir is {local_md_dir}")
def parse_doc(
path_list: list[Path],
output_dir,
lang="ch",
backend="pipeline",
method="auto",
server_url=None,
start_page_id=0, # Start page ID for parsing, default is 0
end_page_id=None # End page ID for parsing, default is None (parse all pages until the end of the document)
):
"""
Parameter description:
path_list: List of document paths to be parsed, can be PDF or image files.
output_dir: Output directory for storing parsing results.
lang: Language option, default is 'ch', optional values include['ch', 'ch_server', 'ch_lite', 'en', 'korean', 'japan', 'chinese_cht', 'ta', 'te', 'ka']。
Input the languages in the pdf (if known) to improve OCR accuracy. Optional.
Adapted only for the case where the backend is set to "pipeline"
backend: the backend for parsing pdf:
pipeline: More general.
vlm-transformers: More general.
vlm-sglang-engine: Faster(engine).
vlm-sglang-client: Faster(client).
without method specified, pipeline will be used by default.
method: the method for parsing pdf:
auto: Automatically determine the method based on the file type.
txt: Use text extraction method.
ocr: Use OCR method for image-based PDFs.
Without method specified, 'auto' will be used by default.
Adapted only for the case where the backend is set to "pipeline".
server_url: When the backend is `sglang-client`, you need to specify the server_url, for example:`http://127.0.0.1:30000`
"""
try:
file_name_list = []
pdf_bytes_list = []
lang_list = []
for path in path_list:
file_name = str(Path(path).stem)
pdf_bytes = read_fn(path)
file_name_list.append(file_name)
pdf_bytes_list.append(pdf_bytes)
lang_list.append(lang)
do_parse(
output_dir=output_dir,
pdf_file_names=file_name_list,
pdf_bytes_list=pdf_bytes_list,
p_lang_list=lang_list,
backend=backend,
parse_method=method,
server_url=server_url,
start_page_id=start_page_id,
end_page_id=end_page_id
)
except Exception as e:
logger.exception(e)
if __name__ == '__main__':
# args
__dir__ = os.path.dirname(os.path.abspath(__file__))
pdf_files_dir = os.path.join(__dir__, "pdfs")
output_dir = os.path.join(__dir__, "output")
pdf_suffixes = [".pdf"]
image_suffixes = [".png", ".jpeg", ".jpg"]
doc_path_list = []
for doc_path in Path(pdf_files_dir).glob('*'):
if doc_path.suffix in pdf_suffixes + image_suffixes:
doc_path_list.append(doc_path)
"""如果您由于网络问题无法下载模型，可以设置环境变量MINERU_MODEL_SOURCE为modelscope使用免代理仓库下载模型"""
os.environ['MINERU_MODEL_SOURCE'] = "modelscope"
"""Use pipeline mode if your environment does not support VLM"""
parse_doc(doc_path_list, output_dir, backend="pipeline")
"""To enable VLM mode, change the backend to 'vlm-xxx'"""
# parse_doc(doc_path_list, output_dir, backend="vlm-transformers") # more general.
# parse_doc(doc_path_list, output_dir, backend="vlm-sglang-engine") # faster(engine).
# parse_doc(doc_path_list, output_dir, backend="vlm-sglang-client", server_url="http://127.0.0.1:30000") # faster(client).
\ No newline at end of file
docker/china/Dockerfile 0 → 100644
View file @ 0fc1daac
# Use the official sglang image
FROM lmsysorg/sglang:v0.4.7-cu124
# install mineru latest
RUN python3 -m pip install -U 'mineru[core]' -i https://mirrors.aliyun.com/pypi/simple --break-system-packages
# Download models and update the configuration file
RUN /bin/bash -c "mineru-models-download -s modelscope -m all"
# Set the entry point to activate the virtual environment and run the command line tool
ENTRYPOINT ["/bin/bash", "-c", "export MINERU_MODEL_SOURCE=local && exec \"$@\"", "--"]
\ No newline at end of file
docker/compose.yaml 0 → 100644
View file @ 0fc1daac
# Documentation:
# https://docs.sglang.ai/backend/server_arguments.html#common-launch-commands
services:
mineru-sglang:
image: mineru-sglang:latest
container_name: mineru-sglang
restart: always
ports:
- 30000:30000
environment:
MINERU_MODEL_SOURCE: local
entrypoint: mineru-sglang-server
command:
--host 0.0.0.0
--port 30000
# --enable-torch-compile # You can also enable torch.compile to accelerate inference speed by approximately 15%
# --dp 2 # If you have more than two GPUs with 24GB VRAM or above, you can use sglang's multi-GPU parallel mode to increase throughput
# --tp 2 # If you have two GPUs with 12GB or 16GB VRAM, you can use the Tensor Parallel (TP) mode
# --mem-fraction-static 0.7 # If you have two GPUs with 11GB VRAM, in addition to Tensor Parallel mode, you need to reduce the KV cache size
ulimits:
memlock: -1
stack: 67108864
ipc: host
healthcheck:
test: ["CMD-SHELL", "curl -f http://localhost:30000/health || exit 1"]
deploy:
resources:
reservations:
devices:
- driver: nvidia
device_ids: ["0"]
capabilities: [gpu]
docker/global/Dockerfile 0 → 100644
View file @ 0fc1daac
# Use the official sglang image
FROM lmsysorg/sglang:v0.4.7-cu124
# install mineru latest
RUN python3 -m pip install -U 'mineru[core]' --break-system-packages
# Download models and update the configuration file
RUN /bin/bash -c "mineru-models-download -s huggingface -m all"
# Set the entry point to activate the virtual environment and run the command line tool
ENTRYPOINT ["/bin/bash", "-c", "export MINERU_MODEL_SOURCE=local && exec \"$@\"", "--"]
\ No newline at end of file
docs/FAQ_en_us.md 0 → 100644
View file @ 0fc1daac
# Frequently Asked Questions
### 1. When using the command `pip install magic-pdf[full]` on newer versions of macOS, the error `zsh: no matches found: magic-pdf[full]` occurs.
On macOS, the default shell has switched from Bash to Z shell, which has special handling logic for certain types of string matching. This can lead to the "no matches found" error. You can try disabling the globbing feature in the command line and then run the installation command again.
```bash
setopt no_nomatch
pip install magic-pdf[full]
```
### 2. Encountering the error `pickle.UnpicklingError: invalid load key, 'v'.` during use
This might be due to an incomplete download of the model file. You can try re-downloading the model file and then try again.
Reference: https://github.com/opendatalab/MinerU/issues/143
### 3. Where should the model files be downloaded and how should the `/models-dir` configuration be set?
The path for the model files is configured in "magic-pdf.json". just like:
```json
{
"models-dir": "/tmp/models"
}
```
This path is an absolute path, not a relative path. You can obtain the absolute path in the models directory using the "pwd" command.
Reference: https://github.com/opendatalab/MinerU/issues/155#issuecomment-2230216874
### 4. Encountered the error `ImportError: libGL.so.1: cannot open shared object file: No such file or directory` in Ubuntu 22.04 on WSL2
The `libgl` library is missing in Ubuntu 22.04 on WSL2. You can install the `libgl` library with the following command to resolve the issue:
```bash
sudo apt-get install libgl1-mesa-glx
```
Reference: https://github.com/opendatalab/MinerU/issues/388
### 5. Encountered error `ModuleNotFoundError: No module named 'fairscale'`
You need to uninstall the module and reinstall it:
```bash
pip uninstall fairscale
pip install fairscale
```
Reference: https://github.com/opendatalab/MinerU/issues/411
### 6. On some newer devices like the H100, the text parsed during OCR using CUDA acceleration is garbled.
The compatibility of cuda11 with new graphics cards is poor, and the CUDA version used by Paddle needs to be upgraded.
```bash
pip install paddlepaddle-gpu==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cu123/
```
Reference: https://github.com/opendatalab/MinerU/issues/558
### 7. On some Linux servers, the program immediately reports an error `Illegal instruction (core dumped)`
This might be because the server's CPU does not support the AVX/AVX2 instruction set, or the CPU itself supports it but has been disabled by the system administrator. You can try contacting the system administrator to remove the restriction or change to a different server.
References: https://github.com/opendatalab/MinerU/issues/591 , https://github.com/opendatalab/MinerU/issues/736
### 8. Error when installing MinerU on CentOS 7 or Ubuntu 18: `ERROR: Failed building wheel for simsimd`
The new version of albumentations (1.4.21) introduces a dependency on simsimd. Since the pre-built package of simsimd for Linux requires a glibc version greater than or equal to 2.28, this causes installation issues on some Linux distributions released before 2019. You can resolve this issue by using the following command:
```
pip install -U magic-pdf[full,old_linux] --extra-index-url https://wheels.myhloli.com
```
Reference: https://github.com/opendatalab/MinerU/issues/1004
### 9. Old Graphics Cards Such as M40 Encounter "RuntimeError: CUDA error: CUBLAS_STATUS_NOT_SUPPORTED"
An error occurs during operation (cuda):
```
RuntimeError: CUDA error: CUBLAS_STATUS_NOT_SUPPORTED when calling cublasGemmStridedBatchedEx(handle, opa, opb, (int)m, (int)n, (int)k, (void*)&falpha, a, CUDA_R_16BF, (int)lda, stridea, b, CUDA_R_16BF, (int)ldb, strideb, (void*)&fbeta, c, CUDA_R_16BF, (int)ldc, stridec, (int)num_batches, compute_type, CUBLAS_GEMM_DEFAULT_TENSOR_OP)
```
Because BF16 precision is not supported on graphics cards before the Turing architecture and some graphics cards are not recognized by torch, it is necessary to manually disable BF16 precision.
Modify the code in lines 287-290 of the "pdf_parse_union_core_v2.py" file (note that the location may vary in different versions):
```
if torch.cuda.is_bf16_supported():
supports_bfloat16 = True
else:
supports_bfloat16 = False
```
Change it to:
```
supports_bfloat16 = False
```
Reference: https://github.com/opendatalab/MinerU/issues/1508
\ No newline at end of file
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