[](https://github.com/opendatalab/MinerU)
[](https://github.com/opendatalab/MinerU)
[](https://github.com/opendatalab/MinerU/issues)
[](https://github.com/opendatalab/MinerU/issues)
[](https://pypi.org/project/mineru/)
[](https://pypi.org/project/mineru/)
[](https://pepy.tech/project/mineru)
[](https://pepy.tech/project/mineru)
[](https://mineru.net/OpenSourceTools/Extractor?source=github)
[](https://huggingface.co/spaces/opendatalab/MinerU)
[](https://www.modelscope.cn/studios/OpenDataLab/MinerU)
[](https://colab.research.google.com/gist/myhloli/a3cb16570ab3cfeadf9d8f0ac91b4fca/mineru_demo.ipynb)
[](https://arxiv.org/abs/2409.18839)
[](https://arxiv.org/abs/2509.22186)
[](https://deepwiki.com/opendatalab/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 domestically and internationally, MinerU is still young. If you encounter any issues or if the results are not as expected, please submit an issue on [GitHub Issues](https://github.com/opendatalab/MinerU/issues) and **attach the relevant PDF**.

## Key Features
- Remove headers, footers, footnotes, page numbers and other elements to ensure semantic coherence
- Output text in human reading order, suitable for single-column, multi-column and complex layouts
- Retain the original document structure, including titles, paragraphs, lists, etc.
- Extract images, image descriptions, tables, table titles and footnotes
- Automatically identify and convert formulas in documents to LaTeX format
- Automatically identify and convert tables in documents to HTML format
- Automatically detect scanned PDFs and garbled PDFs, and enable OCR functionality
- OCR supports detection and recognition of 84 languages
- Support multiple output formats, such as multimodal and NLP Markdown, reading-order-sorted JSON, and information-rich intermediate formats
- Support multiple visualization results, including layout visualization, span visualization, etc., for efficient confirmation of output effects and quality inspection
- Support pure CPU environment operation, and support GPU(CUDA)/NPU(CANN)/MPS acceleration
- Compatible with Windows, Linux and Mac platforms
## User Guide
- [Quick Start Guide](./quick_start/index.md)
- [Detailed Usage Instructions](./usage/index.md)