This article introduces the use of the Python inference engine for the PP-OCR model library. The content is in order of text detection, text recognition, direction classifier and the prediction method of the three in series on the CPU and GPU.
This article introduces the use of the Python inference engine for the PP-OCR model library. The content is in order of text detection, text recognition, direction classifier and the prediction method of the three in series on the CPU and GPU.
-[TEXT DETECTION MODEL INFERENCE](#DETECTION_MODEL_INFERENCE)
-[Text Detection Model Inference](#DETECTION_MODEL_INFERENCE)
-[TEXT RECOGNITION MODEL INFERENCE](#RECOGNITION_MODEL_INFERENCE)
-[Text Recognition Model Inference](#RECOGNITION_MODEL_INFERENCE)
-[1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_RECOGNITION)
-[1. Lightweight Chinese Recognition Model Inference](#LIGHTWEIGHT_RECOGNITION)
-[2. MULTILINGUAL MODEL INFERENCE](MULTILINGUAL_MODEL_INFERENCE)
-[2. Multilingaul Model Inference](#MULTILINGUAL_MODEL_INFERENCE)
-[ANGLE CLASSIFICATION MODEL INFERENCE](#ANGLE_CLASS_MODEL_INFERENCE)
-[Angle Classification Model Inference](#ANGLE_CLASS_MODEL_INFERENCE)
-[TEXT DETECTION ANGLE CLASSIFICATION AND RECOGNITION INFERENCE CONCATENATION](#CONCATENATION)
-[Text Detection Angle Classification and Recognition Inference Concatenation](#CONCATENATION)
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## TEXT DETECTION MODEL INFERENCE
## Text Detection Model Inference
The default configuration is based on the inference setting of the DB text detection model. For lightweight Chinese detection model inference, you can execute the following commands:
The default configuration is based on the inference setting of the DB text detection model. For lightweight Chinese detection model inference, you can execute the following commands:
### 1. LIGHTWEIGHT CHINESE TEXT RECOGNITION MODEL REFERENCE
### 1. Lightweight Chinese Recognition Model Inference
For lightweight Chinese recognition model inference, you can execute the following commands:
For lightweight Chinese recognition model inference, you can execute the following commands:
...
@@ -77,7 +77,7 @@ Predicts of ./doc/imgs_words_en/word_10.png:('PAIN', 0.9897658)
...
@@ -77,7 +77,7 @@ Predicts of ./doc/imgs_words_en/word_10.png:('PAIN', 0.9897658)
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### 2. MULTILINGAUL MODEL INFERENCE
### 2. Multilingaul Model Inference
If you need to predict other language models, when using inference model prediction, you need to specify the dictionary path used by `--rec_char_dict_path`. At the same time, in order to get the correct visualization results,
If you need to predict other language models, when using inference model prediction, you need to specify the dictionary path used by `--rec_char_dict_path`. At the same time, in order to get the correct visualization results,
You need to specify the visual font path through `--vis_font_path`. There are small language fonts provided by default under the `doc/fonts` path, such as Korean recognition:
You need to specify the visual font path through `--vis_font_path`. There are small language fonts provided by default under the `doc/fonts` path, such as Korean recognition:
...
@@ -94,7 +94,7 @@ Predicts of ./doc/imgs_words/korean/1.jpg:('바탕으로', 0.9948904)
...
@@ -94,7 +94,7 @@ Predicts of ./doc/imgs_words/korean/1.jpg:('바탕으로', 0.9948904)
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## ANGLE CLASSIFICATION MODEL INFERENCE
## Angle Classification Model Inference
For angle classification model inference, you can execute the following commands:
For angle classification model inference, you can execute the following commands:
...
@@ -114,7 +114,7 @@ After executing the command, the prediction results (classification angle and sc
...
@@ -114,7 +114,7 @@ After executing the command, the prediction results (classification angle and sc
```
```
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## TEXT DETECTION ANGLE CLASSIFICATION AND RECOGNITION INFERENCE CONCATENATION
## Text Detection Angle Classification and Recognition Inference Concatenation
When performing prediction, you need to specify the path of a single image or a folder of images through the parameter `image_dir`, the parameter `det_model_dir` specifies the path to detect the inference model, the parameter `cls_model_dir` specifies the path to angle classification inference model and the parameter `rec_model_dir` specifies the path to identify the inference model. The parameter `use_angle_cls` is used to control whether to enable the angle classification model. The parameter `use_mp` specifies whether to use multi-process to infer `total_process_num` specifies process number when using multi-process. The parameter . The visualized recognition results are saved to the `./inference_results` folder by default.
When performing prediction, you need to specify the path of a single image or a folder of images through the parameter `image_dir`, the parameter `det_model_dir` specifies the path to detect the inference model, the parameter `cls_model_dir` specifies the path to angle classification inference model and the parameter `rec_model_dir` specifies the path to identify the inference model. The parameter `use_angle_cls` is used to control whether to enable the angle classification model. The parameter `use_mp` specifies whether to use multi-process to infer `total_process_num` specifies process number when using multi-process. The parameter . The visualized recognition results are saved to the `./inference_results` folder by default.
The chapter on PP-OCR model and configuration file mainly adds some basic concepts of OCR model and the content and role of configuration file to have a better experience in the subsequent parameter adjustment and training of the model.
This chapter contains three parts. Firstly, [PP-OCR Model Download](. /models_list_en.md) explains the concept of PP-OCR model types and provides links to download all models. Then in [Yml Configuration](. /config_en.md) details the parameters needed to fine-tune the PP-OCR models. The final [Python Inference for PP-OCR Model Library](. /inference_ppocr_en.md) is an introduction to the use of the PP-OCR model library in the first section, which can quickly utilize the rich model library models to obtain test results through the Python inference engine.
------
Let's first understand some basic concepts.
-[INTRODUCTION ABOUT OCR](#introduction-about-ocr)
-[INTRODUCTION ABOUT OCR](#introduction-about-ocr)
*[BASIC CONCEPTS OF OCR DETECTION MODEL](#basic-concepts-of-ocr-detection-model)
*[BASIC CONCEPTS OF OCR DETECTION MODEL](#basic-concepts-of-ocr-detection-model)
*[Basic concepts of OCR recognition model](#basic-concepts-of-ocr-recognition-model)
*[Basic concepts of OCR recognition model](#basic-concepts-of-ocr-recognition-model)
@@ -95,7 +95,7 @@ If you do not use the provided test image, you can replace the following `--imag
...
@@ -95,7 +95,7 @@ If you do not use the provided test image, you can replace the following `--imag
['PAIN', 0.990372]
['PAIN', 0.990372]
```
```
More whl package usage can be found in [whl package](./whl_en.md)
If you need to use the 2.0 model, please specify the parameter `--version 2.0`, paddleocr uses the 2.1 model by default. More whl package usage can be found in [whl package](./whl_en.md)
If you have a model trained on your own dataset with a different dictionary file, please make sure that you modify the `character_dict_path` in the configuration file to your dictionary file path.
After the conversion is successful, there are three files in the model save directory:
```
inference/det_db/
├── inference.pdiparams # The parameter file of recognition inference model
├── inference.pdiparams.info # The parameter information of recognition inference model, which can be ignored
└── inference.pdmodel # The program file of recognition model
```
- Text recognition model Inference using custom characters dictionary
If the text dictionary is modified during training, when using the inference model to predict, you need to specify the dictionary path used by `--rec_char_dict_path`, and set `rec_char_type=ch`
