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[English](README.md) | [简体中文](README_zh-CN.md)

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<a href="https://github.com/opendatalab/MinerU">MinerU: An end-to-end PDF parsing tool based on PDF-Extract-Kit, supporting conversion from PDF to Markdown.</a>🚀🚀🚀<br>
<a href="https://github.com/opendatalab/PDF-Extract-Kit">PDF-Extract-Kit: A Comprehensive Toolkit for High-Quality PDF Content Extraction</a>🔥🔥🔥
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    👋 join us on <a href="https://discord.gg/AsQMhuMN" target="_blank">Discord</a> and <a href="https://cdn.vansin.top/internlm/mineru.jpg" target="_blank">WeChat</a>
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# MinerU 


## Introduction

MinerU is a one-stop, open-source, high-quality data extraction tool, includes the following primary features:

- [Magic-PDF](#Magic-PDF)  PDF Document Extraction  
- [Magic-Doc](#Magic-Doc)  Webpage & E-book Extraction


# Magic-PDF


## Introduction

Magic-PDF is a tool designed to convert PDF documents into Markdown format, capable of processing files stored locally or on object storage supporting S3 protocol.

Key features include:

- Support for multiple front-end model inputs
- Removal of headers, footers, footnotes, and page numbers
- Human-readable layout formatting
- Retains the original document's structure and formatting, including headings, paragraphs, lists, and more
- Extraction and display of images and tables within markdown
- Conversion of equations into LaTeX format
- Automatic detection and conversion of garbled PDFs
- Compatibility with CPU and GPU environments
- Available for Windows, Linux, and macOS platforms


https://github.com/user-attachments/assets/4bea02c9-6d54-4cd6-97ed-dff14340982c



## Project Panorama

![Project Panorama](docs/images/project_panorama_en.png)


## Flowchart

![Flowchart](docs/images/flowchart_en.png)

### Dependency repositorys

- [PDF-Extract-Kit : A Comprehensive Toolkit for High-Quality PDF Content Extraction](https://github.com/opendatalab/PDF-Extract-Kit) 🚀🚀🚀

## Getting Started

### Requirements

- Python >= 3.9

Using a virtual environment is recommended to avoid potential dependency conflicts; both venv and conda are suitable. 
For example:
```bash
conda create -n MinerU python=3.10
conda activate MinerU
```

### Installation and Configuration

#### 1. Install Magic-PDF

Install the full-feature package with pip:
>Note: The pip-installed package supports CPU-only and is ideal for quick tests.
>
>For CUDA/MPS acceleration in production, see [Acceleration Using CUDA or MPS](#4-Acceleration-Using-CUDA-or-MPS).

```bash
pip install magic-pdf[full-cpu]
```
The full-feature package depends on detectron2, which requires a compilation installation.   
If you need to compile it yourself, please refer to https://github.com/facebookresearch/detectron2/issues/5114  
Alternatively, you can directly use our precompiled whl package (limited to Python 3.10):

```bash
pip install detectron2 --extra-index-url https://myhloli.github.io/wheels/
```


#### 2. Downloading model weights files

For detailed references, please see below [how_to_download_models](docs/how_to_download_models_en.md)

After downloading the model weights, move the 'models' directory to a directory on a larger disk space, preferably an SSD.


#### 3. Copy the Configuration File and Make Configurations
You can get the [magic-pdf.template.json](magic-pdf.template.json) file in the repository root directory.
```bash
cp magic-pdf.template.json ~/magic-pdf.json
```
In magic-pdf.json, configure "models-dir" to point to the directory where the model weights files are located.

```json
{
  "models-dir": "/tmp/models"
}
```


#### 4. Acceleration Using CUDA or MPS
If you have an available Nvidia GPU or are using a Mac with Apple Silicon, you can leverage acceleration with CUDA or MPS respectively.
##### CUDA

You need to install the corresponding PyTorch version according to your CUDA version.  
This example installs the CUDA 11.8 version.More information https://pytorch.org/get-started/locally/  
```bash
pip install --force-reinstall torch==2.3.1 torchvision==0.18.1 --index-url https://download.pytorch.org/whl/cu118
```
Also, you need to modify the value of "device-mode" in the configuration file magic-pdf.json.  
```json
{
  "device-mode":"cuda"
}
```

##### MPS

For macOS users with M-series chip devices, you can use MPS for inference acceleration.  
You also need to modify the value of "device-mode" in the configuration file magic-pdf.json.  
```json
{
  "device-mode":"mps"
}
```


### Usage

#### 1.Usage via Command Line

###### simple

```bash
magic-pdf pdf-command --pdf "pdf_path" --inside_model true
```
After the program has finished, you can find the generated markdown files under the directory "/tmp/magic-pdf".  
You can find the corresponding xxx_model.json file in the markdown directory.   
If you intend to do secondary development on the post-processing pipeline, you can use the command:  
```bash
magic-pdf pdf-command --pdf "pdf_path" --model "model_json_path"
```
In this way, you won't need to re-run the model data, making debugging more convenient.


###### more 

```bash
magic-pdf --help
```


#### 2. Usage via Api

###### Local
```python
image_writer = DiskReaderWriter(local_image_dir)
image_dir = str(os.path.basename(local_image_dir))
jso_useful_key = {"_pdf_type": "", "model_list": []}
pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer)
pipe.pipe_classify()
pipe.pipe_parse()
md_content = pipe.pipe_mk_markdown(image_dir, drop_mode="none")
```

###### Object Storage
```python
s3pdf_cli = S3ReaderWriter(pdf_ak, pdf_sk, pdf_endpoint)
image_dir = "s3://img_bucket/"
s3image_cli = S3ReaderWriter(img_ak, img_sk, img_endpoint, parent_path=image_dir)
pdf_bytes = s3pdf_cli.read(s3_pdf_path, mode=s3pdf_cli.MODE_BIN)
jso_useful_key = {"_pdf_type": "", "model_list": []}
pipe = UNIPipe(pdf_bytes, jso_useful_key, s3image_cli)
pipe.pipe_classify()
pipe.pipe_parse()
md_content = pipe.pipe_mk_markdown(image_dir, drop_mode="none")
```

Demo can be referred to [demo.py](demo/demo.py)


# Magic-Doc


## Introduction

Magic-Doc is a tool designed to convert web pages or multi-format e-books into markdown format.

Key Features Include:

- Web Page Extraction
  - Cross-modal precise parsing of text, images, tables, and formula information.

- E-Book Document Extraction
  - Supports various document formats including epub, mobi, with full adaptation for text and images.

- Language Type Identification
  - Accurate recognition of 176 languages.

https://github.com/opendatalab/MinerU/assets/11393164/a5a650e9-f4c0-463e-acc3-960967f1a1ca



https://github.com/opendatalab/MinerU/assets/11393164/0f4a6fe9-6cca-4113-9fdc-a537749d764d



https://github.com/opendatalab/MinerU/assets/11393164/20438a02-ce6c-4af8-9dde-d722a4e825b2




## Project Repository

- [Magic-Doc](https://github.com/InternLM/magic-doc)
  Outstanding Webpage and E-book Extraction Tool


# All Thanks To Our Contributors

<a href="https://github.com/magicpdf/Magic-PDF/graphs/contributors">
  <img src="https://contrib.rocks/image?repo=opendatalab/MinerU" />
</a>


# License Information

[LICENSE.md](LICENSE.md)

The project currently leverages PyMuPDF to deliver advanced functionalities; however, its adherence to the AGPL license may impose limitations on certain use cases. In upcoming iterations, we intend to explore and transition to a more permissively licensed PDF processing library to enhance user-friendliness and flexibility.


# Acknowledgments

- [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)
- [PyMuPDF](https://github.com/pymupdf/PyMuPDF)
- [fast-langdetect](https://github.com/LlmKira/fast-langdetect)
- [pdfminer.six](https://github.com/pdfminer/pdfminer.six)


# Citation

```bibtex
@misc{2024mineru,
    title={MinerU: A One-stop, Open-source, High-quality Data Extraction Tool},
    author={MinerU Contributors},
    howpublished = {\url{https://github.com/opendatalab/MinerU}},
    year={2024}
}
```


# Star History

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   <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=opendatalab/MinerU&type=Date" />
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</a>

# Links
- [LabelU (A Lightweight Multi-modal Data Annotation Tool)](https://github.com/opendatalab/labelU)
- [LabelLLM (An Open-source LLM Dialogue Annotation Platform)](https://github.com/opendatalab/LabelLLM)
- [PDF-Extract-Kit (A Comprehensive Toolkit for High-Quality PDF Content Extraction)](https://github.com/opendatalab/PDF-Extract-Kit)
