<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/gPxmVeGC" 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 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
>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]==0.6.2b1
```
> ❗️❗️❗️
> We have pre-released the 0.6.2 beta version, addressing numerous issues mentioned in our logs. However, this build has not undergone full QA testing and does not represent the final release quality. Should you encounter any problems, please promptly report them to us via issues or revert to using version 0.6.1.
> ```bash
> pip install magic-pdf[full-cpu]==0.6.1
> ```
#### 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/
> in the command, as these are the highest versions we support. Failing to specify the versions may result in automatically installing higher versions which can cause the program to fail.
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.
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.
