This section provides a tutorial example on how to quickly use, train, and evaluate a key information extraction(KIE) model, [SDMGR](https://arxiv.org/abs/2103.14470), in PaddleOCR.
[SDMGR(Spatial Dual-Modality Graph Reasoning)](https://arxiv.org/abs/2103.14470) is a KIE algorithm that classifies each detected text region into predefined categories, such as order ID, invoice number, amount, and etc.
*[1. 快速使用](#1-----)
*[2. 执行训练](#2-----)
*[3. 执行评估](#3-----)
*[1. Quick Use](#1-----)
*[2. Model Training](#2-----)
*[3. Model Evaluation](#3-----)
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## 1. 快速使用
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：
## 1. Quick Use
```
[Wildreceipt dataset](https://paperswithcode.com/dataset/wildreceipt) is used for this tutorial. It contains 1765 photos, with 25 classes, and 50000 text boxes, which can be downloaded by wget:
The prediction result is saved as `./output/sdmgr_kie/predicts_kie.txt`, and the visualization results are saved in the folder`/output/sdmgr_kie/kie_results/`.
可视化结果如下图所示：
The visualization results are shown in the figure below:
<divalign="center">
<imgsrc="./imgs/0.png"width="800">
</div>
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## 2. 执行训练
## 2. Model Training
创建数据集软链到PaddleOCR/train_data目录下：
```
Create a softlink to the folder, `PaddleOCR/train_data`:
The configuration file used for training is `configs/kie/kie_unet_sdmgr.yml`. The default training data path in the configuration file is `train_data/wildreceipt`. After preparing the data, you can execute the model training with the following command:
This section provides a tutorial example on how to quickly use, train, and evaluate a key information extraction(KIE) model, [SDMGR](https://arxiv.org/abs/2103.14470), in PaddleOCR.
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。
[SDMGR(Spatial Dual-Modality Graph Reasoning)](https://arxiv.org/abs/2103.14470) is a KIE algorithm that classifies each detected text region into predefined categories, such as order ID, invoice number, amount, and etc.
[Wildreceipt dataset](https://paperswithcode.com/dataset/wildreceipt) is used for this tutorial. It contains 1765 photos, with 25 classes, and 50000 text boxes, which can be downloaded by wget:
```shell
```
wget https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/wildreceipt.tar && tar xf wildreceipt.tar
```
Download the pretrained model and predict the result:
执行预测：
```shell
```
cd PaddleOCR/
wget https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar && tar xf kie_vgg16.tar
The prediction result is saved as `./output/sdmgr_kie/predicts_kie.txt`, and the visualization results are saved in the folder`/output/sdmgr_kie/kie_results/`.
The visualization results are shown in the figure below:
可视化结果如下图所示：
<divalign="center">
<imgsrc="./imgs/0.png"width="800">
</div>
<aname="2-----"></a>
## 2. Model Training
## 2. 执行训练
Create a softlink to the folder, `PaddleOCR/train_data`:
```shell
创建数据集软链到PaddleOCR/train_data目录下：
```
cd PaddleOCR/ && mkdir train_data && cd train_data
ln -s ../../wildreceipt ./
```
The configuration file used for training is `configs/kie/kie_unet_sdmgr.yml`. The default training data path in the configuration file is `train_data/wildreceipt`. After preparing the data, you can execute the model training with the following command:
