LayoutLMv3

Microsoft Document AI | GitHub

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 Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, Furu Wei, Preprint 2022.

Results

Dataset	Language	Precision	Recall	F1
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	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) . Portions of the source code are based on the transformers project. Microsoft Open Source Code of Conduct

作者:

Microsoft

数据集大小:

1.04 GB