---
language: zh
license: cc-by-nc-sa-4.0

---
# 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  |   0.8980  | 0.9435 |  0.9202  |  


| Dataset | Subject | Test Time |    Name    | School | Examination Number | Seat Number | Class | Student Number | Grade | Score | **Mean** |        
|---------|:------------|:------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| [EPHOIE](https://github.com/HCIILAB/EPHOIE) |   98.99 | 100.0 | 99.77 | 99.2 | 100.0 | 100.0 | 98.82 | 99.78 | 98.31 | 97.27 | 99.21 |
  
## 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}
}
```

## License

The content of this project itself is licensed under the [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/)
Portions of the source code are based on the [transformers](https://github.com/huggingface/transformers) project.
[Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct)