opensource/PaddleOCR
3
0
Fork 0
mirror of https://github.com/PaddlePaddle/PaddleOCR.git synced 2026-09-24 23:33:08 +08:00
Code Issues Packages Projects Releases Wiki Activity

Update kie_en.md

Browse Source 
1. fix typo
2. add code block's type
This commit is contained in:
RangeKing
2022-01-07 21:47:35 +08:00
parent 9f452b62e9
commit f095671eb6
1 changed files with 7 additions and 7 deletions
Download Patch File Download Diff File Expand all files Collapse all files
ppstructure/docs/kie_en.md
+7 -7
View File
@@ -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
```