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---
language: en
license: mit
---
# 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.
## 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
MIT License.
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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