| # 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} | |
| } | |
| ``` |