README.md

# LayoutLMv3

[Microsoft Document AI](https://www.microsoft.com/en-us/research/project/document-ai/) | [GitHub](https://aka.ms/layoutlmv3)

## Model description

LayoutLMv3 is a pre-trained multimodal Transformer for Document AI with unified text and image masking. The simple unified architecture and training objectives make LayoutLMv3 a general-purpose pre-trained model. For example, LayoutLMv3 can be fine-tuned for both text-centric tasks, including form understanding, receipt understanding, and document visual question answering, and image-centric tasks such as document image classification and document layout analysis.

[LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://arxiv.org/abs/2204.08387)
Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, Furu Wei, Preprint 2022.

## Results
| Dataset | Language | Precision | Recall |    F1    |
|---------|-----------|------------|------|--------|
| [XFUND](https://github.com/doc-analysis/XFUND) | ZH  |   0.8910  | 0.9374 |  0.9136  |  


| Dataset | Subject | Test Time |    Name    | School | Examination Number | Seat Number | Class | Student Number | Grade | Score | **Mean** |        
|---------|:------------|:------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| [EPHOIE](https://github.com/HCIILAB/EPHOIE) |   98.48 | 100 | 99.36 | 98.86 | 100 | 100 | 98.73 | 98.89 | 97.59 | 97.78 | 98.97 |
  
## Citation

If you find LayoutLM useful in your research, please cite the following paper:

```
@article{huang2022layoutlmv3,
  title={LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking},
  author={Yupan Huang and Tengchao Lv and Lei Cui and Yutong Lu and Furu Wei},
  journal={arXiv preprint arXiv:2204.08387},
  year={2022}
}
```