LayoutLM (Document Foundation Model)

Multimodal (text + layout/format + image) pre-training for Document AI

• April, 2021: LayoutXLM is coming by extending the LayoutLM into multilingual support! A multilingual form understanding benchmark XFUND is also introduced, which includes forms with human labeled key-value pairs in 7 languages (Chinese, Japanese, Spanish, French, Italian, German, Portuguese).
• December 29th, 2020: LayoutLMv2 is coming with the new SOTA on a wide varierty of document AI tasks, including DocVQA and SROIE leaderboard.

Introduction

LayoutLM is a simple but effective multi-modal pre-training method of text, layout and image for visually-rich document 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:

LayoutLM: Pre-training of Text and Layout for Document Image Understanding Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou, KDD 2020

LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou, ACL 2021

LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei, Preprint

Release Notes

***** New Sep 27th, 2021: LayoutLM-cased are on HuggingFace *****

***** New Aug 7th, 2020: Our new document understanding datasets, TableBank (LREC 2020) and DocBank (COLING 2020), are now publicly available.*****

***** New May 16th, 2020: Our LayoutLM paper has been accepted to KDD 2020 as a full paper in the research track*****

***** New Feb 18th, 2020: Initial release of pre-trained models and fine-tuning code for LayoutLM v1 *****

Pre-trained Model

We pre-train LayoutLM on IIT-CDIP Test Collection 1.0* dataset.

*As some downstream datasets are the subsets of IIT-CDIP, we have carefully excluded the overlap portion from the pre-training data.

Different Tokenizer

Note that LayoutLM-Base-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') 

Fine-tuning Example on FUNSD

Installation

Please refer to layoutlmft

Command

cd layoutlmft
python -m torch.distributed.launch --nproc_per_node=4 examples/run_funsd.py \
        --model_name_or_path microsoft/layoutlm-base-uncased \
        --output_dir /tmp/test-ner \
        --do_train \
        --do_predict \
        --max_steps 1000 \
        --warmup_ratio 0.1 \
        --fp16

Results

SROIE (field-level)

RVL-CDIP

FUNSD (field-level)

Citation

If you find LayoutLM useful in your research, please cite the following paper:

@inproceedings{Xu2020LayoutLMPO,
  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},
  journal = {Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining},
  year    = {2020}
}

License

This project is licensed under the license found in the LICENSE file in the root directory of this source tree. Portions of the source code are based on the transformers project. Microsoft Open Source Code of Conduct

Contact Information

For help or issues using LayoutLM, please submit a GitHub issue.

For other communications related to LayoutLM, please contact Lei Cui ([email protected]), Furu Wei ([email protected]).