inference_en.md 23.8 KB
Newer Older
K
Khanh Tran 已提交
1

T
tink2123 已提交
2
# Reasoning based on Python prediction engine
K
Khanh Tran 已提交
3

L
licx 已提交
4
The inference model (the model saved by `fluid.io.save_inference_model`) is generally a solidified model saved after the model training is completed, and is mostly used to give prediction in deployment.
K
Khanh Tran 已提交
5 6 7

The model saved during the training process is the checkpoints model, which saves the parameters of the model and is mostly used to resume training.

W
WenmuZhou 已提交
8
Compared with the checkpoints model, the inference model will additionally save the structural information of the model. It has superior performance in predicting in deployment and accelerating inferencing, is flexible and convenient, and is suitable for integration with actual systems. For more details, please refer to the document [Classification Framework](https://github.com/PaddlePaddle/PaddleClas/blob/master/docs/zh_CN/extension/paddle_inference.md).
K
Khanh Tran 已提交
9 10 11

Next, we first introduce how to convert a trained model into an inference model, and then we will introduce text detection, text recognition, and the concatenation of them based on inference model.

L
licx 已提交
12 13 14
- [CONVERT TRAINING MODEL TO INFERENCE MODEL](#CONVERT)
    - [Convert detection model to inference model](#Convert_detection_model)
    - [Convert recognition model to inference model](#Convert_recognition_model)
W
WenmuZhou 已提交
15 16 17
    - [Convert angle classification model to inference model](#Convert_angle_class_model)


L
licx 已提交
18 19 20 21 22
- [TEXT DETECTION MODEL INFERENCE](#DETECTION_MODEL_INFERENCE)
    - [1. LIGHTWEIGHT CHINESE DETECTION MODEL INFERENCE](#LIGHTWEIGHT_DETECTION)
    - [2. DB TEXT DETECTION MODEL INFERENCE](#DB_DETECTION)
    - [3. EAST TEXT DETECTION MODEL INFERENCE](#EAST_DETECTION)
    - [4. SAST TEXT DETECTION MODEL INFERENCE](#SAST_DETECTION)
W
WenmuZhou 已提交
23 24
    - [5. Multilingual model inference](#Multilingual model inference)

L
licx 已提交
25 26 27 28
- [TEXT RECOGNITION MODEL INFERENCE](#RECOGNITION_MODEL_INFERENCE)
    - [1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_RECOGNITION)
    - [2. CTC-BASED TEXT RECOGNITION MODEL INFERENCE](#CTC-BASED_RECOGNITION)
    - [3. ATTENTION-BASED TEXT RECOGNITION MODEL INFERENCE](#ATTENTION-BASED_RECOGNITION)
W
WenmuZhou 已提交
29 30 31 32 33 34 35 36
    - [4. SRN-BASED TEXT RECOGNITION MODEL INFERENCE](#SRN-BASED_RECOGNITION)
    - [5. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY](#USING_CUSTOM_CHARACTERS)
    - [6. MULTILINGUAL MODEL INFERENCE](MULTILINGUAL_MODEL_INFERENCE)

- [ANGLE CLASSIFICATION MODEL INFERENCE](#ANGLE_CLASS_MODEL_INFERENCE)
    - [1. ANGLE CLASSIFICATION MODEL INFERENCE](#ANGLE_CLASS_MODEL_INFERENCE)

- [TEXT DETECTION ANGLE CLASSIFICATION AND RECOGNITION INFERENCE CONCATENATION](#CONCATENATION)
L
licx 已提交
37 38
    - [1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_CHINESE_MODEL)
    - [2. OTHER MODELS](#OTHER_MODELS)
W
WenmuZhou 已提交
39

L
licx 已提交
40
<a name="CONVERT"></a>
X
xxxpsyduck 已提交
41
## CONVERT TRAINING MODEL TO INFERENCE MODEL
L
licx 已提交
42
<a name="Convert_detection_model"></a>
X
xxxpsyduck 已提交
43
### Convert detection model to inference model
K
Khanh Tran 已提交
44

X
xxxpsyduck 已提交
45
Download the lightweight Chinese detection model:
K
Khanh Tran 已提交
46
```
W
WenmuZhou 已提交
47
wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/20-09-22/mobile/det/ch_ppocr_mobile_v1.1_det_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v1.1_det_train.tar -C ./ch_lite/
K
Khanh Tran 已提交
48
```
W
WenmuZhou 已提交
49

K
Khanh Tran 已提交
50 51
The above model is a DB algorithm trained with MobileNetV3 as the backbone. To convert the trained model into an inference model, just run the following command:
```
T
tink2123 已提交
52 53 54 55 56
# -c Set the training algorithm yml configuration file
# -o Set optional parameters
#  Global.checkpoints parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
#  Global.save_inference_dir Set the address where the converted model will be saved.

W
WenmuZhou 已提交
57
python3 tools/export_model.py -c configs/det/det_mv3_db_v1.1.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v1.1_det_train/best_accuracy Global.save_inference_dir=./inference/det_db/
K
Khanh Tran 已提交
58
```
W
WenmuZhou 已提交
59

K
Khanh Tran 已提交
60 61 62 63 64 65 66 67 68
When converting to an inference model, the configuration file used is the same as the configuration file used during training. In addition, you also need to set the `Global.checkpoints` and `Global.save_inference_dir` parameters in the configuration file.
`Global.checkpoints` points to the model parameter file saved during training, and `Global.save_inference_dir` is the directory where the generated inference model is saved.
After the conversion is successful, there are two files in the `save_inference_dir` directory:
```
inference/det_db/
  └─  model     Check the program file of inference model
  └─  params    Check the parameter file of the inference model
```

L
licx 已提交
69
<a name="Convert_recognition_model"></a>
X
xxxpsyduck 已提交
70
### Convert recognition model to inference model
K
Khanh Tran 已提交
71

X
xxxpsyduck 已提交
72
Download the lightweight Chinese recognition model:
K
Khanh Tran 已提交
73
```
W
WenmuZhou 已提交
74
wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/20-09-22/mobile/rec/ch_ppocr_mobile_v1.1_rec_train.tar && tar xf ch_ppocr_mobile_v1.1_rec_train.tar -C ./ch_lite/
K
Khanh Tran 已提交
75 76 77 78
```

The recognition model is converted to the inference model in the same way as the detection, as follows:
```
T
tink2123 已提交
79 80 81 82 83
# -c Set the training algorithm yml configuration file
# -o Set optional parameters
#  Global.checkpoints parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
#  Global.save_inference_dir Set the address where the converted model will be saved.

W
WenmuZhou 已提交
84
python3 tools/export_model.py -c configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v1.1_rec_train/best_accuracy \
K
Khanh Tran 已提交
85 86 87 88 89 90 91 92 93 94 95
```

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 two files in the directory:
```
/inference/rec_crnn/
  └─  model     Identify the saved model files
  └─  params    Identify the parameter files of the inference model
```

W
WenmuZhou 已提交
96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122
<a name="Convert_angle_class_model"></a>
### Convert angle classification model to inference model

Download the angle classification model:
```
wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_v1.1_cls_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v1.1_cls_train.tar -C ./ch_lite/
```

The angle classification model is converted to the inference model in the same way as the detection, as follows:
```
# -c Set the training algorithm yml configuration file
# -o Set optional parameters
#  Global.checkpoints parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
#  Global.save_inference_dir Set the address where the converted model will be saved.

python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v1.1_cls_train/best_accuracy \
        Global.save_inference_dir=./inference/cls/
```

After the conversion is successful, there are two files in the directory:
```
/inference/cls/
  └─  model     Identify the saved model files
  └─  params    Identify the parameter files of the inference model
```


L
licx 已提交
123
<a name="DETECTION_MODEL_INFERENCE"></a>
X
xxxpsyduck 已提交
124
## TEXT DETECTION MODEL INFERENCE
K
Khanh Tran 已提交
125

T
tink2123 已提交
126 127
The following will introduce the lightweight Chinese detection model inference, DB text detection model inference and EAST text detection model inference. The default configuration is based on the inference setting of the DB text detection model.
Because EAST and DB algorithms are very different, when inference, it is necessary to **adapt the EAST text detection algorithm by passing in corresponding parameters**.
K
Khanh Tran 已提交
128

L
licx 已提交
129
<a name="LIGHTWEIGHT_DETECTION"></a>
X
xxxpsyduck 已提交
130
### 1. LIGHTWEIGHT CHINESE DETECTION MODEL INFERENCE
K
Khanh Tran 已提交
131

X
xxxpsyduck 已提交
132
For lightweight Chinese detection model inference, you can execute the following commands:
K
Khanh Tran 已提交
133 134 135 136 137 138 139

```
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/"
```

The visual text detection results are saved to the ./inference_results folder by default, and the name of the result file is prefixed with'det_res'. Examples of results are as follows:

140
![](../imgs_results/det_res_2.jpg)
K
Khanh Tran 已提交
141 142 143 144 145 146 147 148 149 150 151 152

By setting the size of the parameter `det_max_side_len`, the maximum value of picture normalization in the detection algorithm is changed. When the length and width of the picture are less than det_max_side_len, the original picture is used for prediction, otherwise the picture is scaled to the maximum value for prediction. This parameter is set to det_max_side_len=960 by default. If the resolution of the input picture is relatively large and you want to use a larger resolution for prediction, you can execute the following command:

```
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --det_max_side_len=1200
```

If you want to use the CPU for prediction, execute the command as follows
```
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --use_gpu=False
```

L
licx 已提交
153
<a name="DB_DETECTION"></a>
X
xxxpsyduck 已提交
154
### 2. DB TEXT DETECTION MODEL INFERENCE
K
Khanh Tran 已提交
155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173

First, convert the model saved in the DB text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/det_r50_vd_db.tar)), you can use the following command to convert:

```
# Set the yml configuration file of the training algorithm after -c
# The Global.checkpoints parameter sets the address of the training model to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
# The Global.save_inference_dir parameter sets the address where the converted model will be saved.

python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.checkpoints="./models/det_r50_vd_db/best_accuracy" Global.save_inference_dir="./inference/det_db"
```

DB text detection model inference, you can execute the following command:

```
python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_db/"
```

The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows:

174
![](../imgs_results/det_res_img_10_db.jpg)
K
Khanh Tran 已提交
175 176 177

**Note**: Since the ICDAR2015 dataset has only 1,000 training images, mainly for English scenes, the above model has very poor detection result on Chinese text images.

L
licx 已提交
178
<a name="EAST_DETECTION"></a>
X
xxxpsyduck 已提交
179
### 3. EAST TEXT DETECTION MODEL INFERENCE
K
Khanh Tran 已提交
180 181 182 183 184 185 186 187 188 189 190

First, convert the model saved in the EAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/det_r50_vd_east.tar)), you can use the following command to convert:

```
# Set the yml configuration file of the training algorithm after -c
# The Global.checkpoints parameter sets the address of the training model to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
# The Global.save_inference_dir parameter sets the address where the converted model will be saved.

python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.checkpoints="./models/det_r50_vd_east/best_accuracy" Global.save_inference_dir="./inference/det_east"
```

L
licx 已提交
191
**For EAST text detection model inference, you need to set the parameter ``--det_algorithm="EAST"``**, run the following command:
K
Khanh Tran 已提交
192 193 194 195

```
python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_east/" --det_algorithm="EAST"
```
L
licx 已提交
196

K
Khanh Tran 已提交
197 198
The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows:

199
![](../imgs_results/det_res_img_10_east.jpg)
K
Khanh Tran 已提交
200

L
licx 已提交
201 202 203 204 205 206 207 208 209 210 211 212 213
**Note**: EAST post-processing locality aware NMS has two versions: Python and C++. The speed of C++ version is obviously faster than that of Python version. Due to the compilation version problem of NMS of C++ version, C++ version NMS will be called only in Python 3.5 environment, and python version NMS will be called in other cases.


<a name="SAST_DETECTION"></a>
### 4. SAST TEXT DETECTION MODEL INFERENCE
#### (1). Quadrangle text detection model (ICDAR2015)  
First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_icdar2015.tar)), you can use the following command to convert:

```
python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.checkpoints="./models/sast_r50_vd_icdar2015/best_accuracy" Global.save_inference_dir="./inference/det_sast_ic15"
```

**For SAST quadrangle text detection model inference, you need to set the parameter `--det_algorithm="SAST"`**, run the following command:
K
Khanh Tran 已提交
214

L
licx 已提交
215 216 217 218 219
```
python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_sast_ic15/"
```

The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows:
K
Khanh Tran 已提交
220

L
licx 已提交
221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237
![](../imgs_results/det_res_img_10_sast.jpg)

#### (2). Curved text detection model (Total-Text)  
First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the Total-Text English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_total_text.tar)), you can use the following command to convert:

```
python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.checkpoints="./models/sast_r50_vd_total_text/best_accuracy" Global.save_inference_dir="./inference/det_sast_tt"
```

**For SAST curved text detection model inference, you need to set the parameter `--det_algorithm="SAST"` and `--det_sast_polygon=True`**, run the following command:

```
python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/imgs_en/img623.jpg" --det_model_dir="./inference/det_sast_tt/" --det_sast_polygon=True
```

The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows:

M
MissPenguin 已提交
238
![](../imgs_results/det_res_img623_sast.jpg)
L
licx 已提交
239 240 241 242

**Note**: SAST post-processing locality aware NMS has two versions: Python and C++. The speed of C++ version is obviously faster than that of Python version. Due to the compilation version problem of NMS of C++ version, C++ version NMS will be called only in Python 3.5 environment, and python version NMS will be called in other cases.

<a name="RECOGNITION_MODEL_INFERENCE"></a>
X
xxxpsyduck 已提交
243
## TEXT RECOGNITION MODEL INFERENCE
K
Khanh Tran 已提交
244

X
xxxpsyduck 已提交
245
The following will introduce the lightweight Chinese recognition model inference, other CTC-based and Attention-based text recognition models inference. For Chinese text recognition, it is recommended to choose the recognition model based on CTC loss. In practice, it is also found that the result of the model based on Attention loss is not as good as the one based on CTC loss. In addition, if the characters dictionary is modified during training, make sure that you use the same characters set during inferencing. Please check below for details.
K
Khanh Tran 已提交
246 247


L
licx 已提交
248
<a name="LIGHTWEIGHT_RECOGNITION"></a>
X
xxxpsyduck 已提交
249
### 1. LIGHTWEIGHT CHINESE TEXT RECOGNITION MODEL REFERENCE
K
Khanh Tran 已提交
250

X
xxxpsyduck 已提交
251
For lightweight Chinese recognition model inference, you can execute the following commands:
K
Khanh Tran 已提交
252 253 254 255 256

```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="./inference/rec_crnn/"
```

257
![](../imgs_words/ch/word_4.jpg)
K
Khanh Tran 已提交
258 259 260 261 262 263

After executing the command, the prediction results (recognized text and score) of the above image will be printed on the screen.

Predicts of ./doc/imgs_words/ch/word_4.jpg:['实力活力', 0.89552695]


L
licx 已提交
264
<a name="CTC-BASED_RECOGNITION"></a>
X
xxxpsyduck 已提交
265
### 2. CTC-BASED TEXT RECOGNITION MODEL INFERENCE
K
Khanh Tran 已提交
266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283

Taking STAR-Net as an example, we introduce the recognition model inference based on CTC loss. CRNN and Rosetta are used in a similar way, by setting the recognition algorithm parameter `rec_algorithm`.

First, convert the model saved in the STAR-Net text recognition training process into an inference model. Taking the model based on Resnet34_vd backbone network, using MJSynth and SynthText (two English text recognition synthetic datasets) for training, as an example ([model download address](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_ctc.tar)). It can be converted as follow:

```
# Set the yml configuration file of the training algorithm after -c
# The Global.checkpoints parameter sets the address of the training model to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
# The Global.save_inference_dir parameter sets the address where the converted model will be saved.

python3 tools/export_model.py -c configs/rec/rec_r34_vd_tps_bilstm_ctc.yml -o Global.checkpoints="./models/rec_r34_vd_tps_bilstm_ctc/best_accuracy" Global.save_inference_dir="./inference/starnet"
```

For STAR-Net text recognition model inference, execute the following commands:

```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_type="en"
```
X
xxxpsyduck 已提交
284

L
licx 已提交
285
<a name="ATTENTION-BASED_RECOGNITION"></a>
X
xxxpsyduck 已提交
286
### 3. ATTENTION-BASED TEXT RECOGNITION MODEL INFERENCE
287
![](../imgs_words_en/word_336.png)
K
Khanh Tran 已提交
288 289 290 291 292

After executing the command, the recognition result of the above image is as follows:

Predicts of ./doc/imgs_words_en/word_336.png:['super', 0.9999555]

X
xxxpsyduck 已提交
293
**Note**:Since the above model refers to [DTRB](https://arxiv.org/abs/1904.01906) text recognition training and evaluation process, it is different from the training of lightweight Chinese recognition model in two aspects:
K
Khanh Tran 已提交
294 295 296 297 298 299 300 301 302 303

- The image resolution used in training is different: the image resolution used in training the above model is [3,32,100], while during our Chinese model training, in order to ensure the recognition effect of long text, the image resolution used in training is [3, 32, 320]. The default shape parameter of the inference stage is the image resolution used in training phase, that is [3, 32, 320]. Therefore, when running inference of the above English model here, you need to set the shape of the recognition image through the parameter `rec_image_shape`.

- Character list: the experiment in the DTRB paper is only for 26 lowercase English characters and 10 numbers, a total of 36 characters. All upper and lower case characters are converted to lower case characters, and characters not in the above list are ignored and considered as spaces. Therefore, no characters dictionary file is used here, but a dictionary is generated by the below command. Therefore, the parameter `rec_char_type` needs to be set during inference, which is specified as "en" in English.

```
self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
dict_character = list(self.character_str)
```

W
WenmuZhou 已提交
304 305 306 307 308 309 310 311 312 313 314 315 316 317 318
<a name="SRN-BASED_RECOGNITION"></a>
### 4. SRN-BASED TEXT RECOGNITION MODEL INFERENCE

The recognition model based on SRN requires additional setting of the recognition algorithm parameter --rec_algorithm="SRN".
At the same time, it is necessary to ensure that the predicted shape is consistent with the training, such as: --rec_image_shape="1, 64, 256"

```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" \
                                    --rec_model_dir="./inference/srn/" \
                                    --rec_image_shape="1, 64, 256" \
                                    --rec_char_type="en" \
                                    --rec_algorithm="SRN"
```


L
licx 已提交
319
<a name="USING_CUSTOM_CHARACTERS"></a>
W
WenmuZhou 已提交
320
### 5. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY
X
xxxpsyduck 已提交
321
If the chars dictionary is modified during training, you need to specify the new dictionary path by setting the parameter `rec_char_dict_path` when using your inference model to predict.
L
LDOUBLEV 已提交
322 323 324 325 326

```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100" --rec_char_type="en" --rec_char_dict_path="your text dict path"
```

W
WenmuZhou 已提交
327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366
<a name="MULTILINGUAL_MODEL_INFERENCE"></a>
### 6. 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,
You need to specify the visual font path through `--vis_font_path`. There are small language fonts provided by default under the `doc/` path, such as Korean recognition:

```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/korean.ttf"
```
![](../imgs_words/korean/1.jpg)

After executing the command, the prediction result of the above figure is:

``` text
2020-09-19 16:15:05,076-INFO:      index: [205 206  38  39]
2020-09-19 16:15:05,077-INFO:      word : 바탕으로
2020-09-19 16:15:05,077-INFO:      score: 0.9171358942985535
```

<a name="ANGLE_CLASSIFICATION_MODEL_INFERENCE"></a>
## ANGLE CLASSIFICATION MODEL INFERENCE

The following will introduce the angle classification model inference.


<a name="ANGLE_CLASS_MODEL_INFERENCE"></a>
### 1.ANGLE CLASSIFICATION MODEL INFERENCE

For angle classification model inference, you can execute the following commands:

```
python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --cls_model_dir="./inference/cls/"
```

![](../imgs_words/ch/word_4.jpg)

After executing the command, the prediction results (classification angle and score) of the above image will be printed on the screen.

Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999963]


L
licx 已提交
367
<a name="CONCATENATION"></a>
W
WenmuZhou 已提交
368
## TEXT DETECTION ANGLE CLASSIFICATION AND RECOGNITION INFERENCE CONCATENATION
K
Khanh Tran 已提交
369

L
licx 已提交
370
<a name="LIGHTWEIGHT_CHINESE_MODEL"></a>
X
xxxpsyduck 已提交
371
### 1. LIGHTWEIGHT CHINESE MODEL
K
Khanh Tran 已提交
372

W
WenmuZhou 已提交
373
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 visualized recognition results are saved to the `./inference_results` folder by default.
K
Khanh Tran 已提交
374 375

```
W
WenmuZhou 已提交
376 377 378 379 380
# use direction classifier
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=true

# not use use direction classifier
python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/"
K
Khanh Tran 已提交
381 382 383 384
```

After executing the command, the recognition result image is as follows:

385
![](../imgs_results/2.jpg)
K
Khanh Tran 已提交
386

L
licx 已提交
387
<a name="OTHER_MODELS"></a>
X
xxxpsyduck 已提交
388
### 2. OTHER MODELS
K
Khanh Tran 已提交
389

L
licx 已提交
390 391 392 393 394
If you want to try other detection algorithms or recognition algorithms, please refer to the above text detection model inference and text recognition model inference, update the corresponding configuration and model.

**Note: due to the limitation of rotation logic of detected box, SAST curved text detection model (using the parameter `det_sast_polygon=True`) is not supported for model combination yet.**

The following command uses the combination of the EAST text detection and STAR-Net text recognition:
K
Khanh Tran 已提交
395 396 397 398 399 400 401

```
python3 tools/infer/predict_system.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_east/" --det_algorithm="EAST" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_type="en"
```

After executing the command, the recognition result image is as follows:

402
![](../imgs_results/img_10.jpg)