LayoutLM is a simple but effective pre-training method of text and layout for document image understanding and information extraction tasks, such as form understanding and receipt understanding. LayoutLM archives the SOTA results on multiple datasets. For more details, please refer to our paper:
Note that LayoutLM-Cased requires a different tokenizer, based on RobertaTokenizer. You can
initialize it as follows:
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('microsoft/layoutlm-base-cased')
Citation
If you find LayoutLM useful in your research, please cite the following paper:
@misc{xu2019layoutlm,
title={LayoutLM: Pre-training of Text and Layout for Document Image Understanding},
author={Yiheng Xu and Minghao Li and Lei Cui and Shaohan Huang and Furu Wei and Ming Zhou},
year={2019},
eprint={1912.13318},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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