Output will be a list, each item contains classification result and confidence
```bash
['0', 0.99999964]
```
## Use custom model
## Use custom model
When the built-in model cannot meet the needs, you need to use your own trained model.
When the built-in model cannot meet the needs, you need to use your own trained model.
First, refer to the first section of [inference_en.md](./inference_en.md) to convert your det and rec model to inference model, and then use it as follows
First, refer to the first section of [inference_en.md](./inference_en.md) to convert your det and rec model to inference model, and then use it as follows
...
@@ -147,9 +244,9 @@ First, refer to the first section of [inference_en.md](./inference_en.md) to con
...
@@ -147,9 +244,9 @@ First, refer to the first section of [inference_en.md](./inference_en.md) to con
```python
```python
frompaddleocrimportPaddleOCR,draw_ocr
frompaddleocrimportPaddleOCR,draw_ocr
# The path of detection and recognition model must contain model and params files
# The path of detection and recognition model must contain model and params files
| max_text_length | The maximum text length that the recognition algorithm can recognize | 25 |
| max_text_length | The maximum text length that the recognition algorithm can recognize | 25 |
| rec_char_dict_path | the alphabet path which needs to be modified to your own path when `rec_model_Name` use mode 2 | ./ppocr/utils/ppocr_keys_v1.txt |
| rec_char_dict_path | the alphabet path which needs to be modified to your own path when `rec_model_Name` use mode 2 | ./ppocr/utils/ppocr_keys_v1.txt |
| use_space_char | Whether to recognize spaces | TRUE |
| use_space_char | Whether to recognize spaces | TRUE |
| use_angle_cls | Whether to load classification model | FALSE |
| cls_model_dir | the classification inference model folder. There are two ways to transfer parameters, 1. None: Automatically download the built-in model to `~/.paddleocr/cls`; 2. The path of the inference model converted by yourself, the model and params files must be included in the model path | None |
description='Awesome OCR toolkits based on PaddlePaddle (8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices',
description='Awesome OCR toolkits based on PaddlePaddle (8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices',