readme: ---
  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:
  ```
  @inproceedings{huang2022layoutlmv3,
    author={Yupan Huang and Tengchao Lv and Lei Cui and Yutong Lu and Furu Wei},
    title={LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking},
    booktitle={Proceedings of the 30th ACM International Conference on Multimedia},
    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)