magic-pdf 0.6.1

A practical tool for converting PDF to Markdown

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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 PDF Document Extraction
• 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/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

Using a virtual environment is recommended to avoid potential dependency conflicts; both venv and conda are suitable. For example:

conda create -n MinerU python=3.10
conda activate MinerU

Usage Instructions

1. Install Magic-PDF

Install using pip:

pip install magic-pdf

Alternatively, for built-in high-precision model parsing capabilities, use:

pip install magic-pdf[full-cpu]

The high-precision models depend on detectron2, which requires a compiled installation.
If you need to compile it yourself, refer to https://github.com/facebookresearch/detectron2/issues/5114
Or directly use our pre-compiled wheel packages (limited to python 3.10):

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

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 file in the repository 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.
This example installs the CUDA 11.8 version.More information https://pytorch.org/get-started/locally/

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

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
• PyMuPDF
• fast-langdetect
• pdfminer.six

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}
}

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