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--- |
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language: |
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- en |
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- zh |
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license: apache-2.0 |
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task_categories: |
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- image-to-text |
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tags: |
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- ocr |
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- formula-recognition |
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- text-recognition |
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- document-parsing |
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--- |
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<div align="center"> |
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<h1> UniRec40M: Unified Text and Formula Recognition Dataset </h1> |
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[**Paper**](https://huggingface.co/papers/2512.21095) | [**Code**](https://github.com/Topdu/OpenOCR) | [**Demo**](https://huggingface.co/spaces/topdu/OpenOCR-UniRec-Demo) |
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</div> |
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**UniRec40M** is a large-scale dataset comprising 40 million samples of text, formulas, and mixed content. It was introduced in the paper "[UniRec-0.1B: Unified Text and Formula Recognition with 0.1B Parameters](https://huggingface.co/papers/2512.21095)" to enable the training of lightweight yet powerful models for document parsing. |
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The dataset covers multiple levels of recognition, including characters, words, lines, paragraphs, and full documents. It specifically addresses challenges like structural variability and semantic entanglement between text and mathematical formulas. |
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## Features |
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- **Large Scale**: 40 million high-quality samples. |
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- **Unified Recognition**: Supports plain text (words, lines, paragraphs), formulas (single-line, multi-line), and mixed content. |
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- **Bilingual Support**: Comprehensive coverage of Chinese and English documents. |
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- **Multi-domain**: Samples drawn from diverse document types and domains. |
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## Quick Start |
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You can use the associated `openocr-python` package for inference with models trained on this data: |
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```python |
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from openocr import OpenOCR |
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# Initialize the engine (using ONNX as an example) |
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onnx_engine = OpenOCR(backend='onnx', device='cpu') |
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# Path to your image |
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img_path = '/path/to/your/image.png' |
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# Perform recognition |
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result, elapse = onnx_engine(img_path) |
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print(result) |
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``` |
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## Citation |
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If you find this dataset or the UniRec-0.1B model useful for your research, please cite: |
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```bibtex |
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@article{du2025unirec, |
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title={UniRec-0.1B: Unified Text and Formula Recognition with 0.1B Parameters}, |
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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}, |
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journal={arXiv preprint arXiv:2512.21095}, |
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year={2025} |
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} |
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@inproceedings{Du2025SVTRv2, |
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title={SVTRv2: CTC Beats Encoder-Decoder Models in Scene Text Recognition}, |
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author={Yongkun Du and Zhineng Chen and Hongtao Xie and Caiyan Jia and Yu-Gang Jiang}, |
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booktitle={ICCV}, |
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year={2025}, |
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pages={20147-20156} |
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} |
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``` |
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## Acknowledgement |
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This project is maintained by the OCR team from the [FVL Laboratory](https://fvl.fudan.edu.cn), Fudan University. The codebase is built upon [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR), [PytorchOCR](https://github.com/WenmuZhou/PytorchOCR), and [MMOCR](https://github.com/open-mmlab/mmocr). |
