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关键信息抽取算法-VI-LayoutXLM

1. 算法简介

VI-LayoutXLM基于LayoutXLM进行改进，在下游任务训练过程中，去除视觉骨干网络模块，最终精度基本无损的情况下，模型推理速度进一步提升。

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
VI-LayoutXLM VI-LayoutXLM-base SER ser_vi_layoutxlm_xfund_zh_udml.yml 93.19% 训练模型/推理模型
VI-LayoutXLM VI-LayoutXLM-base RE re_vi_layoutxlm_xfund_zh_udml.yml 83.92% 训练模型/推理模型(coming soon)

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

注： 目前RE任务推理过程仍在适配中，下面以SER任务为例，介绍基于VI-LayoutXLM模型的关键信息抽取过程。

首先将训练得到的模型转换成inference model。以VI-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/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_pretrained.tar
tar -xf ser_vi_layoutxlm_xfund_pretrained.tar
python3 tools/export_model.py -c configs/kie/vi_layoutxlm/ser_vi_layoutxlm_xfund_zh.yml -o Architecture.Backbone.checkpoints=./ser_vi_layoutxlm_xfund_pretrained/best_accuracy Global.save_inference_dir=./inference/ser_vi_layoutxlm_infer

VI-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_vi_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 \
  --ocr_order_method="tb-yx"

SER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}
}