2022-02-12 16:17:15 +08:00
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- [关键信息提取(Key Information Extraction)](#关键信息提取key-information-extraction)
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- [1. 快速使用](#1-快速使用)
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- [2. 执行训练](#2-执行训练)
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- [3. 执行评估](#3-执行评估)
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- [4. 参考文献](#4-参考文献)
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2021-12-21 13:17:25 +08:00
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# 关键信息提取(Key Information Extraction)
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本节介绍PaddleOCR中关键信息提取SDMGR方法的快速使用和训练方法。
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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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## 1. 快速使用
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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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```
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wget https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/wildreceipt.tar && tar xf wildreceipt.tar
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```
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执行预测：
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```
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cd PaddleOCR/
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wget https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar && tar xf kie_vgg16.tar
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python3.7 tools/infer_kie.py -c configs/kie/kie_unet_sdmgr.yml -o Global.checkpoints=kie_vgg16/best_accuracy Global.infer_img=../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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2021-12-21 15:55:01 +08:00
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<div align="center">
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<img src="./imgs/0.png" width="800">
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</div>
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2021-12-21 13:17:25 +08:00
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## 2. 执行训练
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创建数据集软链到PaddleOCR/train_data目录下：
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```
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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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训练采用的配置文件是configs/kie/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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```
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python3.7 tools/train.py -c configs/kie/kie_unet_sdmgr.yml -o Global.save_model_dir=./output/kie/
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```
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## 3. 执行评估
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```
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python3.7 tools/eval.py -c configs/kie/kie_unet_sdmgr.yml -o Global.checkpoints=./output/kie/best_accuracy
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```
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2022-02-12 16:17:15 +08:00
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## 4. 参考文献
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2021-12-21 13:17:25 +08:00
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<!-- [ALGORITHM] -->
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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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