# OCRBench & OCRBench v2

**This is the repository of the [OCRBench](./OCRBench/README.md) & [OCRBench v2](./OCRBench_v2/README.md).**

**OCRBench** is a comprehensive evaluation benchmark designed to assess the OCR capabilities of Large Multimodal Models. It comprises five components: Text Recognition, SceneText-Centric VQA, Document-Oriented VQA, Key Information Extraction, and Handwritten Mathematical Expression Recognition. The benchmark includes 1000 question-answer pairs, and all the answers undergo manual verification and correction to ensure a more precise evaluation. More details can be found in [OCRBench README](./OCRBench/README.md).

<p align="center">
  <img src="./OCRBench/images/all_data.png" width="88%" height="80%">
</p>

**OCRBench v2** is a large-scale bilingual text-centric benchmark with currently the most comprehensive set of tasks (4× more tasks than the previous multi-scene benchmark OCRBench), the widest coverage of scenarios (31 diverse scenarios including street scene, receipt, formula, diagram, and so on), and thorough evaluation metrics, with a total of 10, 000 human-verified question-answering pairs and a high proportion of difficult samples. More details can be found in [OCRBench v2 README](./OCRBench_v2/README.md).

<p align="center">
    <img src="https://v1.ax1x.com/2024/12/30/7VhCnP.jpg" width="88%" height="80%">
<p>

# News 
* ```2024.12.31``` 🚀 [OCRBench v2](./OCRBench_v2/README.md) is released.
* ```2024.12.11``` 🚀 OCRBench has been accepted by [Science China Information Sciences](https://link.springer.com/article/10.1007/s11432-024-4235-6).
* ```2024.5.19 ``` 🚀 We realese [DTVQA](https://github.com/ShuoZhang2003/DT-VQA), to explore the Capabilities of Large Multimodal Models on Dense Text.
* ```2024.5.01 ``` 🚀 Thanks to [SWHL](https://github.com/Yuliang-Liu/MultimodalOCR/issues/29) for releasing [ChineseOCRBench](https://huggingface.co/datasets/SWHL/ChineseOCRBench).
* ```2024.3.26 ``` 🚀 OCRBench is now supported in [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval).
* ```2024.3.12 ``` 🚀 We plan to construct OCRBench v2 to include more ocr tasks and data. Any contribution will be appreciated.
* ```2024.2.25 ``` 🚀 OCRBench is now supported in [VLMEvalKit](https://github.com/open-compass/VLMEvalKit).


# Other Related Multilingual Datasets
| Data | Link | Description |
| --- | --- | --- |
| EST-VQA Dataset (CVPR 2020, English and Chinese) | [Link](https://github.com/xinke-wang/EST-VQA) | On the General Value of Evidence, and Bilingual Scene-Text Visual Question Answering. |
| Swahili Dataset (ICDAR 2024) | [Link](https://arxiv.org/abs/2405.11437) | The First Swahili Language Scene Text Detection and Recognition Dataset. |
| Urdu Dataset (ICDAR 2024) | [Link](https://arxiv.org/abs/2405.12533) | Dataset and Benchmark for Urdu Natural Scenes Text Detection, Recognition and Visual Question Answering. |
| MTVQA (9 languages) | [Link](https://arxiv.org/abs/2405.11985) | MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering. |
| EVOBC (Oracle Bone Script Evolution Dataset) | [Link](https://arxiv.org/abs/2401.12467) | We systematically collected ancient characters from authoritative texts and websites spanning six historical stages. |
| HUST-OBC (Oracle Bone Script Character Dataset) | [Link](https://arxiv.org/abs/2401.15365) | For deciphering oracle bone script characters. |

# Citation
If you wish to refer to the baseline results published here, please use the following BibTeX entries:
```BibTeX
@article{Liu_2024,
   title={OCRBench: on the hidden mystery of OCR in large multimodal models},
   volume={67},
   ISSN={1869-1919},
   url={http://dx.doi.org/10.1007/s11432-024-4235-6},
   DOI={10.1007/s11432-024-4235-6},
   number={12},
   journal={Science China Information Sciences},
   publisher={Springer Science and Business Media LLC},
   author={Liu, Yuliang and Li, Zhang and Huang, Mingxin and Yang, Biao and Yu, Wenwen and Li, Chunyuan and Yin, Xu-Cheng and Liu, Cheng-Lin and Jin, Lianwen and Bai, Xiang},
   year={2024},
   month=dec }
```