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| language: en |
| license: cc-by-nc-sa-4.0 |
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| # LayoutLMv3 |
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| [Microsoft Document AI](https://www.microsoft.com/en-us/research/project/document-ai/) | [GitHub](https://aka.ms/layoutlmv3) |
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| ## Model description |
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| 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. |
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| [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, ACM Multimedia 2022. |
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| ## Citation |
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| If you find LayoutLM useful in your research, please cite the following paper: |
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| ``` |
| @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} |
| } |
| ``` |
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| ## License |
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| 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) |
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