diff --git a/ppstructure/docs/kie_en.md b/ppstructure/docs/kie_en.md
index 571903cebc92da764d465df3ac122f93db9c06ab..a424968a9b5a33132afe52a4850cfe541919ae1c 100644
--- a/ppstructure/docs/kie_en.md
+++ b/ppstructure/docs/kie_en.md
@@ -17,21 +17,21 @@ This section provides a tutorial example on how to quickly use, train, and evalu
 
 [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:
 
-```
+```shell
 cd PaddleOCR/
 wget https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar && tar xf kie_vgg16.tar
 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
 ```
 
-The prediction result is saved as the folder`./output/sdmgr_kie/predicts_kie.txt`, and the visualization result is saved as the folder`/output/sdmgr_kie/kie_results/`.
+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 result is shown in the figure below:
+The visualization results are shown in the figure below:
 
 <div align="center">
     <img src="./imgs/0.png" width="800">
@@ -41,14 +41,14 @@ The visualization result is shown in the figure below:
 ## 2. Model Training
 
 Create a softlink to the folder, `PaddleOCR/train_data`:
-```
+```shell
 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:
-```
+```shell
 python3.7 tools/train.py -c configs/kie/kie_unet_sdmgr.yml -o Global.save_model_dir=./output/kie/
 ```
 <a name="3-----"></a>
@@ -57,7 +57,7 @@ python3.7 tools/train.py -c configs/kie/kie_unet_sdmgr.yml -o Global.save_model_
 
 After training, you can execute the model evaluation with the following command:
 
-```
+```shell
 python3.7 tools/eval.py -c configs/kie/kie_unet_sdmgr.yml -o Global.checkpoints=./output/kie/best_accuracy
 ```