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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
Version 3, 19 November 2007
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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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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# MinerU2.5
## 论文
`
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing
`
- https://arxiv.org/abs/2509.22186
## 模型结构
MinerU 2.5 版本重点是介绍自 2.0 版本以来 vlm 端的进展，之前的集成处理方案(Pipeline)并没有大变化。
VLM 的核心创新是采用解耦架构，通过从粗到细两阶段推理机制，将全局布局分析与局部内容识别分离开。
在第一阶段，模型对下采样后的文档图像进行快速且整体的布局分析。
在第二阶段，在检测到的布局指导下，从原始高分辨率输入中裁剪关键区域，并在局部窗口中进行精细识别。
<div align=center>
<img src="./assets/framework.png"/>
</div>
## 算法原理
MinerU 2.5 的模型由三个部分组成：
- 语言模型：因为文档解析任务通常对大规模语言模型的依赖性较低，为了更好地适应裁剪图像解析中不同的分辨率和宽高比， 将 0.5B 参数的 Qwen2-Instruct 模型原有的 1D-RoPE 替换为 M-RoPE ，从而增强了模型在不同分辨率下的泛化能力。
- 视觉编码器：受 Qwen2-VL 的启发，MinerU2.5 集成了一种原生分辨率编码机制。 虽然 Qwen2.5-VL 系列采用窗口注意力机制来提高效率，但这种设计会导致文档解析任务的性能下降。因此，采用基于 Qwen2-VL 初始化的 675M 参数 NaViT。
该视觉编码器支持动态图像分辨率，并采用 2D-RoPE 进行位置编码，使其能够灵活地处理各种分辨率和宽高比的输入。
- 图像块合并器：为了平衡效率和性能，该架构对相邻的 2×2 视觉标记使用像素解混，在将聚合的视觉标记传递给大型语言模型之前对其进行预处理。这种设计有效地实现了计算效率和任务性能之间的权衡。
## 环境配置
### 硬件需求
DCU型号：K100_AI，节点数量：1台，卡数：1张。
`-v 路径`、`docker_name`和`imageID`根据实际情况修改
### Docker（方法一）
```
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.5.1-ubuntu22.04-dtk25.04.2-py3.10
# <your IMAGE ID>为以上拉取的docker的镜像ID替换
docker run -it --name mineru2.5 --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/MinerU2.5_pytorch <your IMAGE ID> /bin/bash
cd /home/MinerU2.5_pytorch
pip install -e .[core] -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
pip install numpy==1.25.0
pip install -e .[all] --no-deps
```
### Dockerfile（方法二）
```
cd /home/MinerU_pytorch
docker build --no-cache -t MinerU2.5:latest .
# <your IMAGE ID>为以上拉取的docker的镜像ID替换
docker run -it --name mineru2.5 --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/MinerU2.5_pytorch <your IMAGE ID> /bin/bash
cd /home/MinerU2.5_pytorch
pip install -e .[core] -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
pip install numpy==1.25.0
pip install -e .[all] --no-deps
```
### 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:dtk25.04.2
python:python3.10
torch:2.5.1
torchvision:0.20.1
triton:3.1
flash-attn:2.6.1
vllm:0.9.2
lmslim:0.3.1
```
`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、其它非特殊库安装,如果特殊深度学习库被替换，请重新安装上述适配版本
```
cd /home/MinerU2.5_pytorch
pip install -e .[core] -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
pip install numpy==1.25.0
pip install -e .[all] --no-deps
```
## 数据集
`无`
## 训练
`无`
## 推理
模型源配置
```
#下载模型设置环境变量：
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
```
cd /home/MinerU2.5_pytorch
# Default parsing using pipeline backend
#<input_path>：本地 PDF/图像文件或目录
#<output_path>：输出目录
HIP_VISIBLE_DEVICES=0 mineru -p <input_path> -o <output_path>
# Or specify vlm backend for parsing
mineru -p <input_path> -o <output_path> -b vlm-transformers
#The vlm backend additionally supports vllm acceleration
mineru -p <input_path> -o <output_path> -b vlm-vllm-engine
#FastAPI calls
#Access http://127.0.0.1:8000/docs in your browser to view the API documentation.
mineru-api --host 0.0.0.0 --port 8000
#Start Gradio WebUI visual frontend
#Access http://127.0.0.1:7860 in your browser to use the Gradio WebUI.
# Using pipeline/vlm-transformers/vlm-http-client backends
mineru-gradio --server-name 0.0.0.0 --server-port 7860
# Or using vlm-vllm-engine/pipeline backends (requires vllm environment)
mineru-gradio --server-name 0.0.0.0 --server-port 7860 --enable-vllm-engine true
#Using http-client/server method:
# Start vllm server (requires vllm environment)
mineru-vllm-server --port 30000
#In another terminal, connect to vllm server via http client (only requires CPU and network, no vllm environment needed)
mineru -p <input_path> -o <output_path> -b vlm-http-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、vllm。
## 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://www.modelscope.cn/models/OpenDataLab/PDF-Extract-Kit-1.0)
transformers/vllm后端模型地址：[OpenDataLab/MinerU2.5-2509-1.2B](https://www.modelscope.cn/models/OpenDataLab/MinerU2.5-2509-1.2B)
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/mineru2.5_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>
<!-- icon -->
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[English](README.md) | [简体中文](README_zh-CN.md)
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# Changelog
- 2025/09/19 2.5.0 Released
We are officially releasing MinerU2.5, currently the most powerful multimodal large model for document parsing.
With only 1.2B parameters, MinerU2.5's accuracy on the OmniDocBench benchmark comprehensively surpasses top-tier multimodal models like Gemini 2.5 Pro, GPT-4o, and Qwen2.5-VL-72B. It also significantly outperforms leading specialized models such as dots.ocr, MonkeyOCR, and PP-StructureV3.
The model has been released on [HuggingFace](https://huggingface.co/opendatalab/MinerU2.5-2509-1.2B) and [ModelScope](https://modelscope.cn/models/opendatalab/MinerU2.5-2509-1.2B) platforms. Welcome to download and use!
- Core Highlights:
- SOTA Performance with Extreme Efficiency: As a 1.2B model, it achieves State-of-the-Art (SOTA) results that exceed models in the 10B and 100B+ classes, redefining the performance-per-parameter standard in document AI.
- Advanced Architecture for Across-the-Board Leadership: By combining a two-stage inference pipeline (decoupling layout analysis from content recognition) with a native high-resolution architecture, it achieves SOTA performance across five key areas: layout analysis, text recognition, formula recognition, table recognition, and reading order.
- Key Capability Enhancements:
- Layout Detection: Delivers more complete results by accurately covering non-body content like headers, footers, and page numbers. It also provides more precise element localization and natural format reconstruction for lists and references.
- Table Parsing: Drastically improves parsing for challenging cases, including rotated tables, borderless/semi-structured tables, and long/complex tables.
- Formula Recognition: Significantly boosts accuracy for complex, long-form, and hybrid Chinese-English formulas, greatly enhancing the parsing capability for mathematical documents.
Additionally, with the release of vlm 2.5, we have made some adjustments to the repository:
- The vlm backend has been upgraded to version 2.5, supporting the MinerU2.5 model and no longer compatible with the MinerU2.0-2505-0.9B model. The last version supporting the 2.0 model is mineru-2.2.2.
- VLM inference-related code has been moved to [mineru_vl_utils](https://github.com/opendatalab/mineru-vl-utils), reducing coupling with the main mineru repository and facilitating independent iteration in the future.
- The vlm accelerated inference framework has been switched from `sglang` to `vllm`, achieving full compatibility with the vllm ecosystem, allowing users to use the MinerU2.5 model and accelerated inference on any platform that supports the vllm framework.
- Due to major upgrades in the vlm model supporting more layout types, we have made some adjustments to the structure of the parsing intermediate file `middle.json` and result file `content_list.json`. Please refer to the [documentation](https://opendatalab.github.io/MinerU/reference/output_files/) for details.
Other repository optimizations:
- Removed file extension whitelist validation for input files. When input files are PDF documents or images, there are no longer requirements for file extensions, improving usability.
<details>
<summary>History Log</summary>
<details>
<summary>2025/09/10 2.2.2 Released</summary>
<ul>
<li>Fixed the issue where the new table recognition model would affect the overall parsing task when some table parsing failed</li>
</ul>
</details>
<details>
<summary>2025/09/08 2.2.1 Released</summary>
<ul>
<li>Fixed the issue where some newly added models were not downloaded when using the model download command.</li>
</ul>
</details>
<details>
<summary>2025/09/05 2.2.0 Released</summary>
<ul>
<li>
Major Updates
<ul>
<li>In this version, we focused on improving table parsing accuracy by introducing a new <a href="https://github.com/RapidAI/TableStructureRec">wired table recognition model</a> and a brand-new hybrid table structure parsing algorithm, significantly enhancing the table recognition capabilities of the <code>pipeline</code> backend.</li>
<li>We also added support for cross-page table merging, which is supported by both <code>pipeline</code> and <code>vlm</code> backends, further improving the completeness and accuracy of table parsing.</li>
</ul>
</li>
<li>
Other Updates
<ul>
<li>The <code>pipeline</code> backend now supports 270-degree rotated table parsing, bringing support for table parsing in 0/90/270-degree orientations</li>
<li><code>pipeline</code> added OCR capability support for Thai and Greek, and updated the English OCR model to the latest version. English recognition accuracy improved by 11%, Thai recognition model accuracy is 82.68%, and Greek recognition model accuracy is 89.28% (by PPOCRv5)</li>
<li>Added <code>bbox</code> field (mapped to 0-1000 range) in the output <code>content_list.json</code>, making it convenient for users to directly obtain position information for each content block</li>
<li>Removed the <code>pipeline_old_linux</code> installation option, no longer supporting legacy Linux systems such as <code>CentOS 7</code>, to provide better support for <code>uv</code>'s <code>sync</code>/<code>run</code> commands</li>
</ul>
</li>
</ul>
</details>
<details>
<summary>2025/08/01 2.1.10 Released</summary>
<ul>
<li>Fixed an issue in the <code>pipeline</code> backend where block overlap caused the parsing results to deviate from expectations #3232</li>
</ul>
</details>
<details>
<summary>2025/07/30 2.1.9 Released</summary>
<ul>
<li><code>transformers</code> 4.54.1 version adaptation</li>
</ul>
</details>
<details>
<summary>2025/07/28 2.1.8 Released</summary>
<ul>
<li><code>sglang</code> 0.4.9.post5 version adaptation</li>
</ul>
</details>
<details>
<summary>2025/07/27 2.1.7 Released</summary>
<ul>
<li><code>transformers</code> 4.54.0 version adaptation</li>
</ul>
</details>
<details>
<summary>2025/07/26 2.1.6 Released</summary>
<ul>
<li>Fixed table parsing issues in handwritten documents when using <code>vlm</code> backend</li>
<li>Fixed visualization box position drift issue when document is rotated #3175</li>
</ul>
</details>
<details>
<summary>2025/07/24 2.1.5 Released</summary>
<ul>
<li><code>sglang</code> 0.4.9 version adaptation, synchronously upgrading the dockerfile base image to sglang 0.4.9.post3</li>
</ul>
</details>
<details>
<summary>2025/07/23 2.1.4 Released</summary>
<ul>
<li><strong>Bug Fixes</strong>
<ul>
<li>Fixed the issue of excessive memory consumption during the <code>MFR</code> step in the <code>pipeline</code> backend under certain scenarios #2771</li>
<li>Fixed the inaccurate matching between <code>image</code>/<code>table</code> and <code>caption</code>/<code>footnote</code> under certain conditions #3129</li>
</ul>
</li>
</ul>
</details>
<details>
<summary>2025/07/16 2.1.1 Released</summary>
<ul>
<li><strong>Bug fixes</strong>
<ul>
<li>Fixed text block content loss issue that could occur in certain <code>pipeline</code> scenarios #3005</li>
<li>Fixed issue where <code>sglang-client</code> required unnecessary packages like <code>torch</code> #2968</li>
<li>Updated <code>dockerfile</code> to fix incomplete text content parsing due to missing fonts in Linux #2915</li>
</ul>
</li>
<li><strong>Usability improvements</strong>
<ul>
<li>Updated <code>compose.yaml</code> to facilitate direct startup of <code>sglang-server</code>, <code>mineru-api</code>, and <code>mineru-gradio</code> services</li>
<li>Launched brand new <a href="https://opendatalab.github.io/MinerU/">online documentation site</a>, simplified readme, providing better documentation experience</li>
</ul>
</li>
</ul>
</details>
<details>
<summary>2025/07/05 2.1.0 Released</summary>
<ul>
<li>This is the first major update of MinerU 2, which includes a large number of new features and improvements, covering significant performance optimizations, user experience enhancements, and bug fixes. The detailed update contents are as follows:</li>
<li><strong>Performance Optimizations:</strong>
<ul>
<li>Significantly improved preprocessing speed for documents with specific resolutions (around 2000 pixels on the long side).</li>
<li>Greatly enhanced post-processing speed when the <code>pipeline</code> backend handles batch processing of documents with fewer pages (&lt;10 pages).</li>
<li>Layout analysis speed of the <code>pipeline</code> backend has been increased by approximately 20%.</li>
</ul>
</li>
<li><strong>Experience Enhancements:</strong>
<ul>
<li>Built-in ready-to-use <code>fastapi service</code> and <code>gradio webui</code>. For detailed usage instructions, please refer to <a href="https://opendatalab.github.io/MinerU/usage/quick_usage/#advanced-usage-via-api-webui-sglang-clientserver">Documentation</a>.</li>
<li>Adapted to <code>sglang</code> version <code>0.4.8</code>, significantly reducing the GPU memory requirements for the <code>vlm-sglang</code> backend. It can now run on graphics cards with as little as <code>8GB GPU memory</code> (Turing architecture or newer).</li>
<li>Added transparent parameter passing for all commands related to <code>sglang</code>, allowing the <code>sglang-engine</code> backend to receive all <code>sglang</code> parameters consistently with the <code>sglang-server</code>.</li>
<li>Supports feature extensions based on configuration files, including <code>custom formula delimiters</code>, <code>enabling heading classification</code>, and <code>customizing local model directories</code>. For detailed usage instructions, please refer to <a href="https://opendatalab.github.io/MinerU/usage/quick_usage/#extending-mineru-functionality-with-configuration-files">Documentation</a>.</li>
</ul>
</li>
<li><strong>New Features:</strong>
<ul>
<li>Updated the <code>pipeline</code> backend with the PP-OCRv5 multilingual text recognition model, supporting text recognition in 37 languages such as French, Spanish, Portuguese, Russian, and Korean, with an average accuracy improvement of over 30%. <a href="https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/algorithm/PP-OCRv5/PP-OCRv5_multi_languages.html">Details</a></li>
<li>Introduced limited support for vertical text layout in the <code>pipeline</code> backend.</li>
</ul>
</li>
</ul>
</details>
<details>
<summary>2025/06/20 2.0.6 Released</summary>
<ul>
<li>Fixed occasional parsing interruptions caused by invalid block content in <code>vlm</code> mode</li>
<li>Fixed parsing interruptions caused by incomplete table structures in <code>vlm</code> mode</li>
</ul>
</details>
<details>
<summary>2025/06/17 2.0.5 Released</summary>
<ul>
<li>Fixed the issue where models were still required to be downloaded in the <code>sglang-client</code> mode</li>
<li>Fixed the issue where the <code>sglang-client</code> mode unnecessarily depended on packages like <code>torch</code> during runtime.</li>
<li>Fixed the issue where only the first instance would take effect when attempting to launch multiple <code>sglang-client</code> instances via multiple URLs within the same process</li>
</ul>
</details>
<details>
<summary>2025/06/15 2.0.3 released</summary>
<ul>
<li>Fixed a configuration file key-value update error that occurred when downloading model type was set to <code>all</code></li>
<li>Fixed the issue where the formula and table feature toggle switches were not working in <code>command line mode</code>, causing the features to remain enabled.</li>
<li>Fixed compatibility issues with sglang version 0.4.7 in the <code>sglang-engine</code> mode.</li>
<li>Updated Dockerfile and installation documentation for deploying the full version of MinerU in sglang environment</li>
</ul>
</details>
<details>
<summary>2025/06/13 2.0.0 Released</summary>
<ul>
<li><strong>New Architecture</strong>: MinerU 2.0 has been deeply restructured in code organization and interaction methods, significantly improving system usability, maintainability, and extensibility.
<ul>
<li><strong>Removal of Third-party Dependency Limitations</strong>: Completely eliminated the dependency on <code>pymupdf</code>, moving the project toward a more open and compliant open-source direction.</li>
<li><strong>Ready-to-use, Easy Configuration</strong>: No need to manually edit JSON configuration files; most parameters can now be set directly via command line or API.</li>
<li><strong>Automatic Model Management</strong>: Added automatic model download and update mechanisms, allowing users to complete model deployment without manual intervention.</li>
<li><strong>Offline Deployment Friendly</strong>: Provides built-in model download commands, supporting deployment requirements in completely offline environments.</li>
<li><strong>Streamlined Code Structure</strong>: Removed thousands of lines of redundant code, simplified class inheritance logic, significantly improving code readability and development efficiency.</li>
<li><strong>Unified Intermediate Format Output</strong>: Adopted standardized <code>middle_json</code> format, compatible with most secondary development scenarios based on this format, ensuring seamless ecosystem business migration.</li>
</ul>
</li>
<li><strong>New Model</strong>: MinerU 2.0 integrates our latest small-parameter, high-performance multimodal document parsing model, achieving end-to-end high-speed, high-precision document understanding.
<ul>
<li><strong>Small Model, Big Capabilities</strong>: With parameters under 1B, yet surpassing traditional 72B-level vision-language models (VLMs) in parsing accuracy.</li>
<li><strong>Multiple Functions in One</strong>: A single model covers multilingual recognition, handwriting recognition, layout analysis, table parsing, formula recognition, reading order sorting, and other core tasks.</li>
<li><strong>Ultimate Inference Speed</strong>: Achieves peak throughput exceeding 10,000 tokens/s through <code>sglang</code> acceleration on a single NVIDIA 4090 card, easily handling large-scale document processing requirements.</li>
<li><strong>Online Experience</strong>: You can experience our brand-new VLM model on <a href="https://mineru.net/OpenSourceTools/Extractor">MinerU.net</a>, <a href="https://huggingface.co/spaces/opendatalab/MinerU">Hugging Face</a>, and <a href="https://www.modelscope.cn/studios/OpenDataLab/MinerU">ModelScope</a>.</li>
</ul>
</li>
<li><strong>Incompatible Changes Notice</strong>: To improve overall architectural rationality and long-term maintainability, this version contains some incompatible changes:
<ul>
<li>Python package name changed from <code>magic-pdf</code> to <code>mineru</code>, and the command-line tool changed from <code>magic-pdf</code> to <code>mineru</code>. Please update your scripts and command calls accordingly.</li>
<li>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.</li>
</ul>
</li>
</ul>
</details>
<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>
# 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>
## Online Experience
### Official online web application
The official online version has the same functionality as the client, with a beautiful interface and rich features, requires login to use
- [![OpenDataLab](https://img.shields.io/badge/webapp_on_mineru.net-blue?logo=data:image/svg+xml;base64,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&labelColor=white)](https://mineru.net/OpenSourceTools/Extractor?source=github)
### Gradio-based online demo
A WebUI developed based on Gradio, with a simple interface and only core parsing functionality, no login required
- [![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)
## 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>
<tr>
<td>Parsing Backend</td>
<td>pipeline</td>
<td>vlm-transformers</td>
<td>vlm-vllm</td>
</tr>
<tr>
<td>Operating System</td>
<td>Linux / Windows / macOS</td>
<td>Linux / Windows</td>
<td>Linux / Windows (via WSL2)</td>
</tr>
<tr>
<td>CPU Inference Support</td>
<td>✅</td>
<td colspan="2">❌</td>
</tr>
<tr>
<td>GPU Requirements</td>
<td>Turing architecture and later, 6GB+ VRAM or Apple Silicon</td>
<td colspan="2">Turing architecture and later, 8GB+ VRAM</td>
</tr>
<tr>
<td>Memory Requirements</td>
<td colspan="3">Minimum 16GB+, recommended 32GB+</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>
</table>
### Install MinerU
#### Install MinerU using pip or uv
```bash
pip install --upgrade pip
pip install uv
uv pip install -U "mineru[core]"
```
#### Install MinerU from source code
```bash
git clone https://github.com/opendatalab/MinerU.git
cd MinerU
uv pip install -e .[core]
```
> [!TIP]
> `mineru[core]` includes all core features except `vLLM` acceleration, compatible with Windows / Linux / macOS systems, suitable for most users.
> If you need to use `vLLM` acceleration for VLM model inference or install a lightweight client on edge devices, please refer to the documentation [Extension Modules Installation Guide](https://opendatalab.github.io/MinerU/quick_start/extension_modules/).
---
#### Deploy MinerU using Docker
MinerU provides a convenient Docker deployment method, which helps quickly set up the environment and solve some tricky environment compatibility issues.
You can get the [Docker Deployment Instructions](https://opendatalab.github.io/MinerU/quick_start/docker_deployment/) in the documentation.
---
### Using MinerU
The simplest command line invocation is:
```bash
mineru -p <input_path> -o <output_path>
```
You can use MinerU for PDF parsing through various methods such as command line, API, and WebUI. For detailed instructions, please refer to the [Usage Guide](https://opendatalab.github.io/MinerU/usage/).
# TODO
- [x] Reading order based on the model
- [x] Recognition of `index` and `list` in the main text
- [x] Table recognition
- [x] Heading Classification
- [x] Handwritten Text Recognition
- [x] Vertical Text Recognition
- [x] Latin Accent Mark Recognition
- [x] Code block recognition in the main text
- [x] [Chemical formula recognition](docs/chemical_knowledge_introduction/introduction.pdf)(mineru.net)
- [ ] 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.
- Limited support for vertical text.
- 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
- If you encounter any issues during usage, you can first check the [FAQ](https://opendatalab.github.io/MinerU/faq/) for solutions.
- If your issue remains unresolved, you may also use [DeepWiki](https://deepwiki.com/opendatalab/MinerU) to interact with an AI assistant, which can address most common problems.
- If you still cannot resolve the issue, you are welcome to join our community via [Discord](https://discord.gg/Tdedn9GTXq) or [WeChat](https://mineru.net/community-portal/?aliasId=3c430f94) to discuss with other users and developers.
# 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)
- [TableStructureRec](https://github.com/RapidAI/TableStructureRec)
- [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)
- [PaddleOCR2Pytorch](https://github.com/frotms/PaddleOCR2Pytorch)
- [layoutreader](https://github.com/ppaanngggg/layoutreader)
- [xy-cut](https://github.com/Sanster/xy-cut)
- [fast-langdetect](https://github.com/LlmKira/fast-langdetect)
- [pypdfium2](https://github.com/pypdfium2-team/pypdfium2)
- [pdftext](https://github.com/datalab-to/pdftext)
- [pdfminer.six](https://github.com/pdfminer/pdfminer.six)
- [pypdf](https://github.com/py-pdf/pypdf)
- [magika](https://github.com/google/magika)
# 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>
# Links
- [Easy Data Preparation with latest LLMs-based Operators and Pipelines](https://github.com/OpenDCAI/DataFlow)
- [Vis3 (OSS browser based on s3)](https://github.com/opendatalab/Vis3)
- [LabelU (A Lightweight Multi-modal Data Annotation Tool)](https://github.com/opendatalab/labelU)
- [LabelLLM (An Open-source LLM Dialogue Annotation Platform)](https://github.com/opendatalab/LabelLLM)
- [PDF-Extract-Kit (A Comprehensive Toolkit for High-Quality PDF Content Extraction)](https://github.com/opendatalab/PDF-Extract-Kit)
- [OmniDocBench (A Comprehensive Benchmark for Document Parsing and Evaluation)](https://github.com/opendatalab/OmniDocBench)
- [Magic-HTML (Mixed web page extraction tool)](https://github.com/opendatalab/magic-html)
- [Magic-Doc (Fast speed ppt/pptx/doc/docx/pdf extraction tool)](https://github.com/InternLM/magic-doc)
- [Dingo: A Comprehensive AI Data Quality Evaluation Tool](https://github.com/MigoXLab/dingo)
\ No newline at end of file
README_zh-CN.md 0 → 100644
View file @ a565fa3a
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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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[![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)
[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/myhloli/a3cb16570ab3cfeadf9d8f0ac91b4fca/mineru_demo.ipynb)
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[English](README.md) | [简体中文](README_zh-CN.md)
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🚀<a href="https://mineru.net/?source=github">MinerU 官网入口→✅ 免装在线版 ✅ 全功能客户端 ✅ 开发者API在线调用，省去部署麻烦，多种产品形态一键get，速冲！</a>
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👋 join us on <a href="https://discord.gg/Tdedn9GTXq" target="_blank">Discord</a> and <a href="https://mineru.net/community-portal/?aliasId=3c430f94" target="_blank">WeChat</a>
</p>
</div>
# 更新记录
- 2025/09/19 2.5.0 发布
我们正式发布 MinerU2.5，当前最强文档解析多模态大模型。仅凭 1.2B 参数，MinerU2.5 在 OmniDocBench 文档解析评测中，精度已全面超越 Gemini2.5-Pro、GPT-4o、Qwen2.5-VL-72B等顶级多模态大模型，并显著领先于主流文档解析专用模型（如 dots.ocr, MonkeyOCR, PP-StructureV3 等）。
模型已发布至[HuggingFace](https://huggingface.co/opendatalab/MinerU2.5-2509-1.2B)和[ModelScope](https://huggingface.co/opendatalab/MinerU2.5-2509-1.2B)平台，欢迎大家下载使用！
- 核心亮点
- 极致能效，性能SOTA: 以 1.2B 的轻量化规模，实现了超越百亿乃至千亿级模型的SOTA性能，重新定义了文档解析的能效比。
- 先进架构，全面领先: 通过 “两阶段推理” (解耦布局分析与内容识别) 与 原生高分辨率架构 的结合，在布局分析、文本识别、公式识别、表格识别及阅读顺序五大方面均达到 SOTA 水平。
- 关键能力提升
- 布局检测: 结果更完整，精准覆盖页眉、页脚、页码等非正文内容；同时提供更精准的元素定位与更自然的格式还原（如列表、参考文献）。
- 表格解析: 大幅优化了对旋转表格、无线/少线表、以及长难表格的解析能力。
- 公式识别: 显著提升中英混合公式及复杂长公式的识别准确率，大幅改善数学类文档解析能力。
此外，伴随vlm 2.5的发布，我们对仓库做出一些调整：
- vlm后端升级至2.5版本，支持MinerU2.5模型，不再兼容MinerU2.0-2505-0.9B模型，最后一个支持2.0模型的版本为mineru-2.2.2。
- vlm推理相关代码已移至[mineru_vl_utils](https://github.com/opendatalab/mineru-vl-utils),降低与mineru主仓库的耦合度，便于后续独立迭代。
- vlm加速推理框架从`sglang`切换至`vllm`,并实现对vllm生态的完全兼容，使得用户可以在任何支持vllm框架的平台上使用MinerU2.5模型并加速推理。
- 由于vlm模型的重大升级，支持更多layout type，因此我们对解析的中间文件`middle.json`和结果文件`content_list.json`的结构做出一些调整，请参考[文档](https://opendatalab.github.io/MinerU/zh/reference/output_files/)了解详情。
其他仓库优化：
- 移除对输入文件的后缀名白名单校验，当输入文件为PDF文档或图片时，对文件的后缀名不再有要求，提升易用性。
<details>
<summary>历史日志</summary>
<details>
<summary>2025/09/10 2.2.2 发布</summary>
<ul>
<li>修复新的表格识别模型在部分表格解析失败时影响整体解析任务的问题</li>
</ul>
</details>
<details>
<summary>2025/09/08 2.2.1 发布</summary>
<ul>
<li>修复使用模型下载命令时，部分新增模型未下载的问题</li>
</ul>
</details>
<details>
<summary>2025/09/05 2.2.0 发布</summary>
<ul>
<li>
主要更新
<ul>
<li>在这个版本我们重点提升了表格的解析精度，通过引入新的<a href="https://github.com/RapidAI/TableStructureRec">有线表识别模型</a>和全新的混合表格结构解析算法，显著提升了<code>pipeline</code>后端的表格识别能力。</li>
<li>另外我们增加了对跨页表格合并的支持，这一功能同时支持<code>pipeline</code>和<code>vlm</code>后端，进一步提升了表格解析的完整性和准确性。</li>
</ul>
</li>
<li>
其他更新
<ul>
<li><code>pipeline</code>后端增加270度旋转的表格解析能力，现已支持0/90/270度三个方向的表格解析</li>
<li><code>pipeline</code>增加对泰文、希腊文的ocr能力支持，并更新了英文ocr模型至最新，英文识别精度提升11%，泰文识别模型精度 82.68%，希腊文识别模型精度 89.28%（by PPOCRv5）</li>
<li>在输出的<code>content_list.json</code>中增加了<code>bbox</code>字段(映射至0-1000范围内)，方便用户直接获取每个内容块的位置信息</li>
<li>移除<code>pipeline_old_linux</code>安装可选项，不再支持老版本的Linux系统如<code>Centos 7</code>等，以便对<code>uv</code>的<code>sync</code>/<code>run</code>等命令进行更好的支持</li>
</ul>
</li>
</ul>
</details>
<details>
<summary>2025/08/01 2.1.10 发布</summary>
<ul>
<li>修复<code>pipeline</code>后端因block覆盖导致的解析结果与预期不符 #3232</li>
</ul>
</details>
<details>
<summary>2025/07/30 2.1.9 发布</summary>
<ul>
<li><code>transformers</code> 4.54.1 版本适配</li>
</ul>
</details>
<details>
<summary>2025/07/28 2.1.8 发布</summary>
<ul>
<li><code>sglang</code> 0.4.9.post5 版本适配</li>
</ul>
</details>
<details>
<summary>2025/07/27 2.1.7 发布</summary>
<ul>
<li><code>transformers</code> 4.54.0 版本适配</li>
</ul>
</details>
<details>
<summary>2025/07/26 2.1.6 发布</summary>
<ul>
<li>修复<code>vlm</code>后端解析部分手写文档时的表格异常问题</li>
<li>修复文档旋转时可视化框位置漂移问题 #3175</li>
</ul>
</details>
<details>
<summary>2025/07/24 2.1.5 发布</summary>
<ul>
<li><code>sglang</code> 0.4.9 版本适配，同步升级dockerfile基础镜像为sglang 0.4.9.post3</li>
</ul>
</details>
<details>
<summary>2025/07/23 2.1.4 发布</summary>
<ul>
<li><strong>bug修复</strong>
<ul>
<li>修复<code>pipeline</code>后端中<code>MFR</code>步骤在某些情况下显存消耗过大的问题 #2771</li>
<li>修复某些情况下<code>image</code>/<code>table</code>与<code>caption</code>/<code>footnote</code>匹配不准确的问题 #3129</li>
</ul>
</li>
</ul>
</details>
<details>
<summary>2025/07/16 2.1.1 发布</summary>
<ul>
<li><strong>bug修复</strong>
<ul>
<li>修复<code>pipeline</code>在某些情况可能发生的文本块内容丢失问题 #3005</li>
<li>修复<code>sglang-client</code>需要安装<code>torch</code>等不必要的包的问题 #2968</li>
<li>更新<code>dockerfile</code>以修复linux字体缺失导致的解析文本内容不完整问题 #2915</li>
</ul>
</li>
<li><strong>易用性更新</strong>
<ul>
<li>更新<code>compose.yaml</code>，便于用户直接启动<code>sglang-server</code>、<code>mineru-api</code>、<code>mineru-gradio</code>服务</li>
<li>启用全新的<a href="https://opendatalab.github.io/MinerU/zh/">在线文档站点</a>，简化readme，提供更好的文档体验</li>
</ul>
</li>
</ul>
</details>
<details>
<summary>2025/07/05 2.1.0 发布</summary>
<p>这是 MinerU 2 的第一个大版本更新，包含了大量新功能和改进，包含众多性能优化、体验优化和bug修复，具体更新内容如下：</p>
<ul>
<li><strong>性能优化：</strong>
<ul>
<li>大幅提升某些特定分辨率（长边2000像素左右）文档的预处理速度</li>
<li>大幅提升<code>pipeline</code>后端批量处理大量页数较少（&lt;10）文档时的后处理速度</li>
<li><code>pipeline</code>后端的layout分析速度提升约20%</li>
</ul>
</li>
<li><strong>体验优化：</strong>
<ul>
<li>内置开箱即用的<code>fastapi服务</code>和<code>gradio webui</code>，详细使用方法请参考<a href="https://opendatalab.github.io/MinerU/zh/usage/quick_usage/#apiwebuisglang-clientserver">文档</a></li>
<li><code>sglang</code>适配<code>0.4.8</code>版本，大幅降低<code>vlm-sglang</code>后端的显存要求，最低可在<code>8G显存</code>(Turing及以后架构)的显卡上运行</li>
<li>对所有命令增加<code>sglang</code>的参数透传，使得<code>sglang-engine</code>后端可以与<code>sglang-server</code>一致，接收<code>sglang</code>的所有参数</li>
<li>支持基于配置文件的功能扩展，包含<code>自定义公式标识符</code>、<code>开启标题分级功能</code>、<code>自定义本地模型目录</code>，详细使用方法请参考<a href="https://opendatalab.github.io/MinerU/zh/usage/quick_usage/#mineru_1">文档</a></li>
</ul>
</li>
<li><strong>新特性：</strong>
<ul>
<li><code>pipeline</code>后端更新 PP-OCRv5 多语种文本识别模型，支持法语、西班牙语、葡萄牙语、俄语、韩语等 37 种语言的文字识别，平均精度涨幅超30%。<a href="https://paddlepaddle.github.io/PaddleOCR/latest/version3.x/algorithm/PP-OCRv5/PP-OCRv5_multi_languages.html">详情</a></li>
<li><code>pipeline</code>后端增加对竖排文本的有限支持</li>
</ul>
</li>
</ul>
</details>
<details>
<summary>2025/06/20 2.0.6发布</summary>
<ul>
<li>修复<code>vlm</code>模式下，某些偶发的无效块内容导致解析中断问题</li>
<li>修复<code>vlm</code>模式下，某些不完整的表结构导致的解析中断问题</li>
</ul>
</details>
<details>
<summary>2025/06/17 2.0.5发布</summary>
<ul>
<li>修复了<code>sglang-client</code>模式下依然需要下载模型的问题</li>
<li>修复了<code>sglang-client</code>模式需要依赖<code>torch</code>等实际运行不需要的包的问题</li>
<li>修复了同一进程内尝试通过多个url启动多个<code>sglang-client</code>实例时，只有第一个生效的问题</li>
</ul>
</details>
<details>
<summary>2025/06/15 2.0.3发布</summary>
<ul>
<li>修复了当下载模型类型设置为<code>all</code>时，配置文件出现键值更新错误的问题</li>
<li>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</li>
<li>修复了<code>sglang-engine</code>模式下，0.4.7版本sglang的兼容性问题</li>
<li>更新了sglang环境下部署完整版MinerU的Dockerfile和相关安装文档</li>
</ul>
</details>
<details>
<summary>2025/06/13 2.0.0发布</summary>
<ul>
<li><strong>全新架构</strong>：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。
<ul>
<li><strong>去除第三方依赖限制</strong>：彻底移除对 <code>pymupdf</code> 的依赖，推动项目向更开放、合规的开源方向迈进。</li>
<li><strong>开箱即用，配置便捷</strong>：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。</li>
<li><strong>模型自动管理</strong>：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。</li>
<li><strong>离线部署友好</strong>：提供内置模型下载命令，支持完全断网环境下的部署需求。</li>
<li><strong>代码结构精简</strong>：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。</li>
<li><strong>统一中间格式输出</strong>：采用标准化的 <code>middle_json</code> 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。</li>
</ul>
</li>
<li><strong>全新模型</strong>：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。
<ul>
<li><strong>小模型，大能力</strong>：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）。</li>
<li><strong>多功能合一</strong>：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。</li>
<li><strong>极致推理速度</strong>：在单卡 NVIDIA 4090 上通过 <code>sglang</code> 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。</li>
<li><strong>在线体验</strong>：您可以在<a href="https://mineru.net/OpenSourceTools/Extractor">MinerU.net</a>、<a href="https://huggingface.co/spaces/opendatalab/MinerU">Hugging Face</a>, 以及<a href="https://www.modelscope.cn/studios/OpenDataLab/MinerU">ModelScope</a>体验我们的全新VLM模型</li>
</ul>
</li>
<li><strong>不兼容变更说明</strong>: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：
<ul>
<li>Python 包名从 <code>magic-pdf</code> 更改为 <code>mineru</code>，命令行工具也由 <code>magic-pdf</code> 改为 <code>mineru</code>，请同步更新脚本与调用命令。</li>
<li>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。</li>
</ul>
</li>
</ul>
</details>
<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>
# 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
如果安装或使用中遇到任何问题，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>
## 在线体验
### 官网在线应用
官网在线版功能与客户端一致，界面美观，功能丰富，需要登录使用
- [![OpenDataLab](https://img.shields.io/badge/webapp_on_mineru.net-blue?logo=data:image/svg+xml;base64,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&labelColor=white)](https://mineru.net/OpenSourceTools/Extractor?source=github)
### 基于Gradio的在线demo
基于gradio开发的webui，界面简洁，仅包含核心解析功能，免登录
- [![ModelScope](https://img.shields.io/badge/Demo_on_ModelScope-purple?logo=data:image/svg+xml;base64,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&labelColor=white)](https://www.modelscope.cn/studios/OpenDataLab/MinerU)
- [![HuggingFace](https://img.shields.io/badge/Demo_on_HuggingFace-yellow.svg?logo=data:image/png;base64,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&labelColor=white)](https://huggingface.co/spaces/opendatalab/MinerU)
## 本地部署
> [!WARNING]
> **安装前必看——软硬件环境支持说明**
>
> 为了确保项目的稳定性和可靠性，我们在开发过程中仅对特定的软硬件环境进行优化和测试。这样当用户在推荐的系统配置上部署和运行项目时，能够获得最佳的性能表现和最少的兼容性问题。
>
> 通过集中资源和精力于主线环境，我们团队能够更高效地解决潜在的BUG，及时开发新功能。
>
> 在非主线环境中，由于硬件、软件配置的多样性，以及第三方依赖项的兼容性问题，我们无法100%保证项目的完全可用性。因此，对于希望在非推荐环境中使用本项目的用户，我们建议先仔细阅读文档以及FAQ，大多数问题已经在FAQ中有对应的解决方案，除此之外我们鼓励社区反馈问题，以便我们能够逐步扩大支持范围。
<table>
<tr>
<td>解析后端</td>
<td>pipeline</td>
<td>vlm-transformers</td>
<td>vlm-vllm</td>
</tr>
<tr>
<td>操作系统</td>
<td>Linux / Windows / macOS</td>
<td>Linux / Windows</td>
<td>Linux / Windows (via WSL2)</td>
</tr>
<tr>
<td>CPU推理支持</td>
<td>✅</td>
<td colspan="2">❌</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 colspan="2">Turing及以后架构，8G显存以上</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>
</table>
### 安装 MinerU
#### 使用pip或uv安装MinerU
```bash
pip install --upgrade pip -i https://mirrors.aliyun.com/pypi/simple
pip install uv -i https://mirrors.aliyun.com/pypi/simple
uv pip install -U "mineru[core]" -i https://mirrors.aliyun.com/pypi/simple
```
#### 通过源码安装MinerU
```bash
git clone https://github.com/opendatalab/MinerU.git
cd MinerU
uv pip install -e .[core] -i https://mirrors.aliyun.com/pypi/simple
```
> [!TIP]
> `mineru[core]`包含除`vLLM`加速外的所有核心功能，兼容Windows / Linux / macOS系统，适合绝大多数用户。
> 如果您有使用`vLLM`加速VLM模型推理，或是在边缘设备安装轻量版client端等需求，可以参考文档[扩展模块安装指南](https://opendatalab.github.io/MinerU/zh/quick_start/extension_modules/)。
---
#### 使用docker部署Mineru
MinerU提供了便捷的docker部署方式，这有助于快速搭建环境并解决一些棘手的环境兼容问题。
您可以在文档中获取[Docker部署说明](https://opendatalab.github.io/MinerU/zh/quick_start/docker_deployment/)。
---
### 使用 MinerU
最简单的命令行调用方式:
```bash
mineru -p <input_path> -o <output_path>
```
您可以通过命令行、API、WebUI等多种方式使用MinerU进行PDF解析，具体使用方法请参考[使用指南](https://opendatalab.github.io/MinerU/zh/usage/)。
# TODO
- [x] 基于模型的阅读顺序
- [x] 正文中目录、列表识别
- [x] 表格识别
- [x] 标题分级
- [x] 手写文本识别
- [x] 竖排文本识别
- [x] 拉丁字母重音符号识别
- [x] 正文中代码块识别
- [x] [化学式识别](docs/chemical_knowledge_introduction/introduction.pdf)(https://mineru.net)
- [ ] 图表内容识别
# Known Issues
- 阅读顺序基于模型对可阅读内容在空间中的分布进行排序，在极端复杂的排版下可能会部分区域乱序
- 对竖排文字的支持较为有限
- 目录和列表通过规则进行识别，少部分不常见的列表形式可能无法识别
- 代码块在layout模型里还没有支持
- 漫画书、艺术图册、小学教材、习题尚不能很好解析
- 表格识别在复杂表格上可能会出现行/列识别错误
- 在小语种PDF上，OCR识别可能会出现字符不准确的情况（如阿拉伯文易混淆字符等）
- 部分公式可能会无法在markdown中渲染
# FAQ
- 如果您在使用过程中遇到问题，可以先查看[常见问题](https://opendatalab.github.io/MinerU/zh/faq/)是否有解答。
- 如果未能解决您的问题，您也可以使用[DeepWiki](https://deepwiki.com/opendatalab/MinerU)与AI助手交流，这可以解决大部分常见问题。
- 如果您仍然无法解决问题，您可通过[Discord](https://discord.gg/Tdedn9GTXq)或[WeChat](https://mineru.net/community-portal/?aliasId=3c430f94)加入社区，与其他用户和开发者交流。
# All Thanks To Our Contributors
<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)
- [TableStructureRec](https://github.com/RapidAI/TableStructureRec)
- [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)
- [PaddleOCR2Pytorch](https://github.com/frotms/PaddleOCR2Pytorch)
- [layoutreader](https://github.com/ppaanngggg/layoutreader)
- [xy-cut](https://github.com/Sanster/xy-cut)
- [fast-langdetect](https://github.com/LlmKira/fast-langdetect)
- [pypdfium2](https://github.com/pypdfium2-team/pypdfium2)
- [pdftext](https://github.com/datalab-to/pdftext)
- [pdfminer.six](https://github.com/pdfminer/pdfminer.six)
- [pypdf](https://github.com/py-pdf/pypdf)
- [magika](https://github.com/google/magika)
# 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>
# Links
- [Easy Data Preparation with latest LLMs-based Operators and Pipelines](https://github.com/OpenDCAI/DataFlow)
- [Vis3 (OSS browser based on s3)](https://github.com/opendatalab/Vis3)
- [LabelU (A Lightweight Multi-modal Data Annotation Tool)](https://github.com/opendatalab/labelU)
- [LabelLLM (An Open-source LLM Dialogue Annotation Platform)](https://github.com/opendatalab/LabelLLM)
- [PDF-Extract-Kit (A Comprehensive Toolkit for High-Quality PDF Content Extraction)](https://github.com/opendatalab/PDF-Extract-Kit)
- [OmniDocBench (A Comprehensive Benchmark for Document Parsing and Evaluation)](https://github.com/opendatalab/OmniDocBench)
- [Magic-HTML (Mixed web page extraction tool)](https://github.com/opendatalab/magic-html)
- [Magic-Doc (Fast speed ppt/pptx/doc/docx/pdf extraction tool)](https://github.com/InternLM/magic-doc)
- [Dingo: A Comprehensive AI Data Quality Evaluation Tool](https://github.com/MigoXLab/dingo)
\ No newline at end of file
SECURITY.md 0 → 100644
View file @ a565fa3a
# 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 @ a565fa3a
# 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.guess_suffix_or_lang import guess_suffix_by_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'
formula_enable=True, # Enable formula parsing
table_enable=True, # Enable table parsing
server_url=None, # Server URL for vlm-http-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=formula_enable,table_enable=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, formula_enable)
pdf_info = middle_json["pdf_info"]
pdf_bytes = pdf_bytes_list[idx]
_process_output(
pdf_info, pdf_bytes, pdf_file_name, local_md_dir, local_image_dir,
md_writer, f_draw_layout_bbox, f_draw_span_bbox, f_dump_orig_pdf,
f_dump_md, f_dump_content_list, f_dump_middle_json, f_dump_model_output,
f_make_md_mode, middle_json, model_json, is_pipeline=True
)
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"]
_process_output(
pdf_info, pdf_bytes, pdf_file_name, local_md_dir, local_image_dir,
md_writer, f_draw_layout_bbox, f_draw_span_bbox, f_dump_orig_pdf,
f_dump_md, f_dump_content_list, f_dump_middle_json, f_dump_model_output,
f_make_md_mode, middle_json, infer_result, is_pipeline=False
)
def _process_output(
pdf_info,
pdf_bytes,
pdf_file_name,
local_md_dir,
local_image_dir,
md_writer,
f_draw_layout_bbox,
f_draw_span_bbox,
f_dump_orig_pdf,
f_dump_md,
f_dump_content_list,
f_dump_middle_json,
f_dump_model_output,
f_make_md_mode,
middle_json,
model_output=None,
is_pipeline=True
):
"""处理输出文件"""
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,
)
image_dir = str(os.path.basename(local_image_dir))
if f_dump_md:
make_func = pipeline_union_make if is_pipeline else vlm_union_make
md_content_str = make_func(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:
make_func = pipeline_union_make if is_pipeline else vlm_union_make
content_list = make_func(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_output, ensure_ascii=False, indent=4),
)
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,
end_page_id=None
):
"""
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-vllm-engine: Faster(engine).
vlm-http-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 `http-client`, you need to specify the server_url, for example:`http://127.0.0.1:30000`
start_page_id: Start page ID for parsing, default is 0
end_page_id: End page ID for parsing, default is None (parse all pages until the end of the document)
"""
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", "jp2", "webp", "gif", "bmp", "jpg"]
doc_path_list = []
for doc_path in Path(pdf_files_dir).glob('*'):
if guess_suffix_by_path(doc_path) 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-vllm-engine") # faster(engine).
# parse_doc(doc_path_list, output_dir, backend="vlm-http-client", server_url="http://127.0.0.1:30000") # faster(client).
\ No newline at end of file
docker/Dockerfile 0 → 100644
View file @ a565fa3a
FROM image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.5.1-ubuntu22.04-dtk25.04.2-py3.10
\ No newline at end of file
docs/en/demo/index.md 0 → 100644
View file @ a565fa3a
<script type="module" src="https://gradio.s3-us-west-2.amazonaws.com/5.35.0/gradio.js"></script>
<gradio-app src="https://opendatalab-mineru.hf.space"></gradio-app>
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