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---
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typora-copy-images-to: images
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comments: true
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---
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# 关键信息抽取算法-SDMGR
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## 1. 算法简介
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论文信息：
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> [Spatial Dual-Modality Graph Reasoning for Key Information Extraction](https://arxiv.org/abs/2103.14470)
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>
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> Hongbin Sun and Zhanghui Kuang and Xiaoyu Yue and Chenhao Lin and Wayne Zhang
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>
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> 2021
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在wildreceipt发票公开数据集上，算法复现效果如下：
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|模型|骨干网络|配置文件|hmean|下载链接|
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| --- | --- | --- | --- | --- |
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|SDMGR|VGG6|[configs/kie/sdmgr/kie_unet_sdmgr.yml](../../configs/kie/sdmgr/kie_unet_sdmgr.yml)|86.70%|[训练模型]( https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar)/[推理模型(coming soon)]()|
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## 2. 环境配置
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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。
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## 3. 模型训练、评估、预测
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SDMGR是一个关键信息提取算法，将每个检测到的文本区域分类为预定义的类别，如订单ID、发票号码，金额等。
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训练和测试的数据采用wildreceipt数据集，通过如下指令下载数据集：
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```bash linenums="1"
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wget https://paddleocr.bj.bcebos.com/ppstructure/dataset/wildreceipt.tar && tar xf wildreceipt.tar
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```
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创建数据集软链到PaddleOCR/train_data目录下：
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```bash linenums="1"
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cd PaddleOCR/ && mkdir train_data && cd train_data
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ln -s ../../wildreceipt ./
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```
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### 3.1 模型训练
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训练采用的配置文件是`configs/kie/sdmgr/kie_unet_sdmgr.yml`，配置文件中默认训练数据路径是`train_data/wildreceipt`，准备好数据后，可以通过如下指令执行训练：
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```bash linenums="1"
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python3 tools/train.py -c configs/kie/sdmgr/kie_unet_sdmgr.yml -o Global.save_model_dir=./json/kie/
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```
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### 3.2 模型评估
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执行下面的命令进行模型评估
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```bash linenums="1"
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python3 tools/eval.py -c configs/kie/sdmgr/kie_unet_sdmgr.yml -o Global.checkpoints=./json/kie/best_accuracy
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```
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输出信息示例如下所示:
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```bash linenums="1"
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[2022/08/10 05:22:23] ppocr INFO: metric eval ***************
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[2022/08/10 05:22:23] ppocr INFO: hmean:0.8670120239257812
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[2022/08/10 05:22:23] ppocr INFO: fps:10.18816520530961
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```
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### 3.3 模型预测
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执行下面的命令进行模型预测，预测的时候需要预先加载存储图片路径以及OCR信息的文本文件，使用`Global.infer_img`进行指定。
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```bash linenums="1"
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python3 tools/infer_kie.py -c configs/kie/kie_unet_sdmgr.yml -o Global.checkpoints=kie_vgg16/best_accuracy Global.infer_img=./train_data/wildreceipt/1.txt
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```
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执行预测后的结果保存在`./output/sdmgr_kie/predicts_kie.txt`文件中，可视化结果保存在`/output/sdmgr_kie/kie_results/`目录下。
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可视化结果如下图所示：
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## 4. 推理部署
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### 4.1 Python推理
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暂不支持
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### 4.2 C++推理部署
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暂不支持
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### 4.3 Serving服务化部署
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暂不支持
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### 4.4 更多推理部署
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暂不支持
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## 5. FAQ
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## 引用
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```bibtex
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@misc{sun2021spatial,
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title={Spatial Dual-Modality Graph Reasoning for Key Information Extraction},
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author={Hongbin Sun and Zhanghui Kuang and Xiaoyu Yue and Chenhao Lin and Wayne Zhang},
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year={2021},
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eprint={2103.14470},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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