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 | 0.8910 | 0.9374 | 0.9136 |
Dataset | Subject | Test Time | Name | School | Examination Number | Seat Number | Class | Student Number | Grade | Score | Mean |
---|---|---|---|---|---|---|---|---|---|---|---|
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}
}