Please refer to [quick installation](./installation_en.md) to configure the PaddleOCR operating environment.
Please refer to [quick installation](./installation_en.md) to configure the PaddleOCR operating environment.
* Note: Support the use of PaddleOCR through whl package installation,pelease refer [PaddleOCR Package](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/whl_en.md).
* Note: Support the use of PaddleOCR through whl package installation,pelease refer [PaddleOCR Package](./whl_en.md).
## 2.inference models
## 2.inference models
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@@ -14,8 +14,8 @@ The detection and recognition models on the mobile and server sides are as follo
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@@ -14,8 +14,8 @@ The detection and recognition models on the mobile and server sides are as follo
| Model introduction | Model name | Recommended scene | Detection model | Direction Classifier | Recognition model |
| Model introduction | Model name | Recommended scene | Detection model | Direction Classifier | Recognition model |
* If `wget` is not installed in the windows environment, you can copy the link to the browser to download when downloading the model, then uncompress it and place it in the corresponding directory.
* If `wget` is not installed in the windows environment, you can copy the link to the browser to download when downloading the model, then uncompress it and place it in the corresponding directory.
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@@ -37,11 +37,11 @@ Take the ultra-lightweight model as an example:
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```
```
mkdir inference && cd inference
mkdir inference && cd inference
# Download the detection model of the ultra-lightweight Chinese OCR model and uncompress it
# Download the detection model of the ultra-lightweight Chinese OCR model and uncompress it
wget https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_infer.tar && tar xf ch_ppocr_mobile_v1.1_det_infer.tar
wget link && tar xf ch_ppocr_mobile_v1.1_det_infer.tar
# Download the recognition model of the ultra-lightweight Chinese OCR model and uncompress it
# Download the recognition model of the ultra-lightweight Chinese OCR model and uncompress it
wget https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_infer.tar && tar xf ch_ppocr_mobile_v1.1_rec_infer.tar
wget link && tar xf ch_ppocr_mobile_v1.1_rec_infer.tar
# Download the direction classifier model of the ultra-lightweight Chinese OCR model and uncompress it
# Download the direction classifier model of the ultra-lightweight Chinese OCR model and uncompress it
wget https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_infer.tar && tar xf ch_ppocr_mobile_v1.1_cls_infer.tar
wget link && tar xf ch_ppocr_mobile_v1.1_cls_infer.tar
cd ..
cd ..
```
```
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@@ -63,7 +63,7 @@ After decompression, the file structure should be as follows:
...
@@ -63,7 +63,7 @@ After decompression, the file structure should be as follows:
## 3. Single image or image set prediction
## 3. Single image or image set prediction
* The following code implements text detection and recognition process. When performing prediction, you need to specify the path of a single image or image set through the parameter `image_dir`, the parameter `det_model_dir` specifies the path to detect the inference model, the parameter `rec_model_dir` specifies the path to identify the inference model, the parameter `use_angle_cls` specifies whether to use the direction classifier, the parameter `cls_model_dir` specifies the path to identify the direction classifier model, the parameter `use_space_char` specifies whether to predict the space char. The visual results are saved to the `./inference_results` folder by default.
* The following code implements text detection、angle class and recognition process. When performing prediction, you need to specify the path of a single image or image set through the parameter `image_dir`, the parameter `det_model_dir` specifies the path to detect the inference model, the parameter `rec_model_dir` specifies the path to identify the inference model, the parameter `use_angle_cls` specifies whether to use the direction classifier, the parameter `cls_model_dir` specifies the path to identify the direction classifier model, the parameter `use_space_char` specifies whether to predict the space char. The visual results are saved to the `./inference_results` folder by default.