8.6 KiB
MinerU
Introduction
MinerU is a one-stop, open-source, high-quality data extraction tool, includes the following primary features:
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/opendatalab/MinerU/assets/11393164/618937cb-dc6a-4646-b433-e3131a5f4070
Project Panorama
Flowchart
Submodule Repositories
- PDF-Extract-Kit
- A Comprehensive Toolkit for High-Quality PDF Content Extraction
Getting Started
Requirements
- Python >= 3.9
It is recommended to use a virtual environment, either with venv or conda. Development is based on Python 3.10, should you encounter problems with other Python versions, please switch to Python 3.10.
Usage Instructions
1. Install Magic-PDF
# If you only need the basic features (without built-in model parsing functionality)
pip install magic-pdf
# or
# For complete parsing capabilities (including high-precision model parsing)
pip install magic-pdf[full-cpu]
# Additionally, you will need to install the dependency detectron2.
# For detectron2, compile it yourself as per https://github.com/facebookresearch/detectron2/issues/5114
# Or use our precompiled wheel
# windows
pip install https://github.com/opendatalab/MinerU/raw/master/assets/whl/detectron2-0.6-cp310-cp310-win_amd64.whl
# linux
pip install https://github.com/opendatalab/MinerU/raw/master/assets/whl/detectron2-0.6-cp310-cp310-linux_x86_64.whl
# macOS(Intel)
pip install https://github.com/opendatalab/MinerU/raw/master/assets/whl/detectron2-0.6-cp310-cp310-macosx_10_9_universal2.whl
# macOS(M1/M2/M3)
pip install https://github.com/opendatalab/MinerU/raw/master/assets/whl/detectron2-0.6-cp310-cp310-macosx_11_0_arm64.whl
2. Downloading model weights files
For detailed references, please see belowhow_to_download_models
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
# Copy the configuration file to the root directory
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.
{
"models-dir": "/tmp/models"
}
4. Usage via Command Line
simple
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:
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
magic-pdf --help
5. Acceleration Using CUDA or MPS
CUDA
You need to install the corresponding PyTorch version according to your CUDA version.
# When using the GPU solution, you need to reinstall PyTorch for the corresponding CUDA version. This example installs the CUDA 11.8 version.
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.
{
"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.
{
"device-mode":"mps"
}
6. Usage via Api
Local
image_writer = DiskReaderWriter(local_image_dir)
image_dir = str(os.path.basename(local_image_dir))
jso_useful_key = {"_pdf_type": "", "model_list": model_json}
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
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": model_json}
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
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 Outstanding Webpage and E-book Extraction Tool
All Thanks To Our Contributors
License Information
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
Citation
@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}
}