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# OpenDoc-0.1B: Ultra-Lightweight Document Parsing System with 0.1B Parameters
- \[[Quick Start](#quick-start)\] \[[ModelScope Demo](https://www.modelscope.cn/studios/topdktu/OpenDoc-Demo)\] \[[Hugging Face Demo](https://huggingface.co/spaces/topdu/OpenDoc-Demo)\] \[[Local Demo](#local-demo)\]
## Introduction
**OpenDoc-0.1B** is an ultra-lightweight document parsing system featuring only 0.1 billion parameters. It operates through a sophisticated two-stage pipeline: first, it utilizes [PP-DocLayoutV2](https://www.paddleocr.ai/latest/version3.x/module_usage/layout_analysis.html) for precise layout analysis; second, it employs an enhanced, in-house [UniRec-0.1B](./unirec.md) model for the unified recognition of text, formulas, and tables. While the original version of UniRec-0.1B focused solely on text and formulas, this rebuilt iteration integrates comprehensive table recognition capabilities. Supporting both Chinese and English document parsing, **OpenDoc-0.1B** achieves an impressive **90.57%** score on [OmniDocBench (v1.5)](https://github.com/opendatalab/OmniDocBench/tree/main?tab=readme-ov-file#end-to-end-evaluation), demonstrating superior performance that outrivals many large-scale multimodal document parsing models.
## Quick Start
### Requirements
```bash
conda create -n openocr python=3.10
conda activate openocr
git clone https://github.com/Topdu/OpenOCR.git
cd OpenOCR
pip install -r requirements.txt
python -m pip install paddlepaddle-gpu==3.2.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/
python -m pip install paddlex
pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu126
pip install transformers==4.49.0
```
### Download UniRec-0.1B model
```bash
# download model from modelscope
modelscope download topdktu/unirec-0.1b --local_dir ./unirec-0.1b
# or download model from huggingface
huggingface-cli download topdu/unirec-0.1b --local-dir ./unirec-0.1b
```
### Inference
```bash
# cpu
python tools/infer_doc.py --input_path ../doc_img_or_pdf --output_path ./output --gpus -1
# gpu
python tools/infer_doc.py --input_path ../doc_img_or_pdf --output_path ./output --gpus 0
# multi gpu
python tools/infer_doc.py --input_path ../doc_img_or_pdf --output_path ./output --gpus 0,1,2,3,4,5,6,7
```
## Local Demo
```shell
pip install gradio==4.20.0
python demo_opendoc.py
```
## Citation
If you find our method useful for your research, please cite:
```bibtex
@article{du2025unirec,
title={UniRec-0.1B: Unified Text and Formula Recognition with 0.1B Parameters},
author={Yongkun Du and Zhineng Chen and Yazhen Xie and Weikang Bai and Hao Feng and Wei Shi and Yuchen Su and Can Huang and Yu-Gang Jiang},
journal={arXiv preprint arXiv:2512.21095},
year={2025}
}
```
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