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\[[Paper](https://arxiv.org/pdf/2512.21095)\] \[[ModelScope Model](https://www.modelscope.cn/models/topdktu/unirec-0.1b)\] \[[HuggingFace Model](https://huggingface.co/topdu/unirec-0.1b)\] \[[ModelScope Demo](https://www.modelscope.cn/studios/topdktu/OpenOCR-UniRec-Demo)\] \[[Hugging Face Demo](https://huggingface.co/spaces/topdu/OpenOCR-UniRec-Demo)\] \[[Local Demo](#local-demo)\]
## Introduction
**UniRec** is good at recognizing plain text (words, lines, paragraphs), formulas (single-line, multi-line), and mixed text-and-formulas content. You only need to use a screenshot tool to select the text area from a paper and paste it into the \[[ModelScope Demo](https://www.modelscope.cn/studios/topdktu/OpenOCR-UniRec-Demo)\] or \[[Hugging Face Demo](https://huggingface.co/spaces/topdu/OpenOCR-UniRec-Demo)\]. After clicking Run, the recognition will be completed automatically
## Get Started with the UniRec
### Dependencies:
- [PyTorch](http://pytorch.org/) version >= 1.13.0
- Python version >= 3.7
```shell
conda create -n openocr python==3.9
conda activate openocr
# install gpu version torch >=1.13.0
conda install pytorch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0 pytorch-cuda=11.8 -c pytorch -c nvidia
# or cpu version
conda install pytorch torchvision torchaudio cpuonly -c pytorch
git clone https://github.com/Topdu/OpenOCR.git
```
### Downloding the UniRec Model from ModelScope or Hugging Face
```shell
cd OpenOCR
pip install -r requirements.txt
# 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
```shell
python tools/infer_rec.py --c ./configs/rec/unirec/focalsvtr_ardecoder_unirec.yml --o Global.infer_img=/path/img_fold or /path/img_file
```
### Local Demo
```shell
pip install gradio==4.20.0
python demo_unirec.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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