algorithm_kie_layoutxlm.md 6.8 KB
关键信息抽取算法-LayoutXLM
1. 算法简介
论文信息：
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
在XFUND_zh数据集上，算法复现效果如下：
|模型|骨干网络|任务|配置文件|hmean|下载链接| | --- | --- |--|--- | --- | --- | |LayoutXLM|LayoutXLM-base|SER |ser_layoutxlm_xfund_zh.yml|90.38%|训练模型/推理模型| |LayoutXLM|LayoutXLM-base|RE | re_layoutxlm_xfund_zh.yml|74.83%|训练模型/推理模型|
2. 环境配置
请先参考《运行环境准备》配置PaddleOCR运行环境，参考《项目克隆》克隆项目代码。
3. 模型训练、评估、预测
请参考关键信息抽取教程。PaddleOCR对代码进行了模块化，训练不同的关键信息抽取模型只需要更换配置文件即可。
4. 推理部署
4.1 Python推理
- SER
首先将训练得到的模型转换成inference model。LayoutXLM模型在XFUND_zh数据集上训练的模型为例（模型下载地址），可以使用下面的命令进行转换。
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
LayoutXLM模型基于SER任务进行推理，可以执行如下命令：
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
SER可视化结果默认保存到./output文件夹里面，结果示例如下：
- RE
首先将训练得到的模型转换成inference model。LayoutXLM模型在XFUND_zh数据集上训练的模型为例（模型下载地址），可以使用下面的命令进行转换。
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/ser_layoutxlm_infer
LayoutXLM模型基于RE任务进行推理，可以执行如下命令：
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
RE可视化结果默认保存到./output文件夹里面，结果示例如下：
4.2 C++推理部署
暂不支持
4.3 Serving服务化部署
暂不支持
4.4 更多推理部署
暂不支持
5. FAQ
引用
@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}
}