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+---
+comments: true
+---
+
+# 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](https://arxiv.org/abs/2104.08836)
+>
+> 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](https://github.com/PaddlePaddle/PaddleOCR/tree/main/configs/kie/layoutlm_series/ser_layoutxlm_xfund_zh.yml)|90.38%|[训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar)/[推理模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh_infer.tar)|
+|LayoutXLM|LayoutXLM-base|RE | [re_layoutxlm_xfund_zh.yml](https://github.com/PaddlePaddle/PaddleOCR/tree/main/configs/kie/layoutlm_series/re_layoutxlm_xfund_zh.yml)|74.83%|[训练模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar)/[推理模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh_infer.tar)|
+
+## 2. 环境配置
+
+请先参考[《运行环境准备》](../../ppocr/environment.md)配置PaddleOCR运行环境，参考[《项目克隆》](../../ppocr/blog/clone.md)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](../../ppocr/model_train/kie.md)。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](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar)），可以使用下面的命令进行转换。
+
+``` bash
+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：
+
+```bash linenums="1"
+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：
+
+![](./images/zh_val_42_ser.jpg)
+
+#### 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](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar)），可以使用下面的命令进行转换。
+
+``` bash
+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：
+
+```bash linenums="1"
+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：
+
+![](./images/zh_val_42_re.jpg)
+
+### 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
+
+```bibtex
+@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}
+}
+```