DocumentAIKit/doc/doc_en/algorithm_kie_layoutxlm_en.md

KIE Algorithm - LayoutXLM

• 1. Introduction
• 2. Environment
• 3. Model Training / Evaluation / Prediction
• 4. Inference and Deployment
  • 4.1 Python Inference
  • 4.2 C++ Inference
  • 4.3 Serving
  • 4.4 More
• 5. FAQ
• Citation

1. Introduction

Paper:

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

2021

On XFUND_zh dataset, the algorithm reproduction Hmean is as follows.

|Model|Backbone|Task |Cnnfig|Hmean|Download link| | --- | --- |--|--- | --- | --- | |LayoutXLM|LayoutXLM-base|SER |ser_layoutxlm_xfund_zh.yml|90.38%|trained model/inference model| |LayoutXLM|LayoutXLM-base|RE | re_layoutxlm_xfund_zh.yml|74.83%|trained model/inference model|

2. Environment

Please refer to "Environment Preparation" to configure the PaddleOCR environment, and refer to "Project Clone" to clone the project code.

3. Model Training / Evaluation / Prediction

Please refer to KIE tutorial。PaddleOCR has modularized the code structure, so that you only need to replace the configuration file to train different models.

4. Inference and Deployment

4.1 Python Inference

• SER

First, we need to export the trained model into inference model. Take LayoutXLM model trained on XFUND_zh as an example (trained model download link). Use the following command to export.

wget https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar
tar -xf ser_LayoutXLM_xfun_zh.tar
python3 tools/export_model.py -c configs/kie/layoutlm_series/ser_layoutxlm_xfund_zh.yml -o Architecture.Backbone.checkpoints=./ser_LayoutXLM_xfun_zh Global.save_inference_dir=./inference/ser_layoutxlm_infer

Use the following command to infer using LayoutXLM SER model.

cd ppstructure
python3 kie/predict_kie_token_ser.py \
  --kie_algorithm=LayoutXLM \
  --ser_model_dir=../inference/ser_layoutxlm_infer \
  --image_dir=./docs/kie/input/zh_val_42.jpg \
  --ser_dict_path=../train_data/XFUND/class_list_xfun.txt \
  --vis_font_path=../doc/fonts/simfang.ttf

The SER visualization results are saved in the ./output directory by default. The results are as follows.

• RE

First, we need to export the trained model into inference model. Take LayoutXLM model trained on XFUND_zh as an example (trained model download link). Use the following command to export.

wget https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar
tar -xf re_LayoutXLM_xfun_zh.tar
python3 tools/export_model.py -c configs/kie/layoutlm_series/re_layoutxlm_xfund_zh.yml -o Architecture.Backbone.checkpoints=./re_LayoutXLM_xfun_zh Global.save_inference_dir=./inference/re_layoutxlm_infer

Use the following command to infer using LayoutXLM RE model.

cd ppstructure
python3 kie/predict_kie_token_ser_re.py \
  --kie_algorithm=LayoutXLM \
  --re_model_dir=../inference/re_layoutxlm_infer \
  --ser_model_dir=../inference/ser_layoutxlm_infer \
  --image_dir=./docs/kie/input/zh_val_42.jpg \
  --ser_dict_path=../train_data/XFUND/class_list_xfun.txt \
  --vis_font_path=../doc/fonts/simfang.ttf

The RE visualization results are saved in the ./output directory by default. The results are as follows.

4.2 C++ Inference

Not supported

4.3 Serving

Not supported

4.4 More

Not supported

5. FAQ

Citation

@article{DBLP:journals/corr/abs-2104-08836,
  author    = {Yiheng Xu and
               Tengchao Lv and
               Lei Cui and
               Guoxin Wang and
               Yijuan Lu and
               Dinei Flor{\^{e}}ncio and
               Cha Zhang and
               Furu Wei},
  title     = {LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich
               Document Understanding},
  journal   = {CoRR},
  volume    = {abs/2104.08836},
  year      = {2021},
  url       = {https://arxiv.org/abs/2104.08836},
  eprinttype = {arXiv},
  eprint    = {2104.08836},
  timestamp = {Thu, 14 Oct 2021 09:17:23 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2104-08836.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-1912-13318,
  author    = {Yiheng Xu and
               Minghao Li and
               Lei Cui and
               Shaohan Huang and
               Furu Wei and
               Ming Zhou},
  title     = {LayoutLM: Pre-training of Text and Layout for Document Image Understanding},
  journal   = {CoRR},
  volume    = {abs/1912.13318},
  year      = {2019},
  url       = {http://arxiv.org/abs/1912.13318},
  eprinttype = {arXiv},
  eprint    = {1912.13318},
  timestamp = {Mon, 01 Jun 2020 16:20:46 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1912-13318.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-2012-14740,
  author    = {Yang Xu and
               Yiheng Xu and
               Tengchao Lv and
               Lei Cui and
               Furu Wei and
               Guoxin Wang and
               Yijuan Lu and
               Dinei A. F. Flor{\^{e}}ncio and
               Cha Zhang and
               Wanxiang Che and
               Min Zhang and
               Lidong Zhou},
  title     = {LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding},
  journal   = {CoRR},
  volume    = {abs/2012.14740},
  year      = {2020},
  url       = {https://arxiv.org/abs/2012.14740},
  eprinttype = {arXiv},
  eprint    = {2012.14740},
  timestamp = {Tue, 27 Jul 2021 09:53:52 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2012-14740.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}