recognition_en.md 19.8 KB
Newer Older
X
xxxpsyduck 已提交
1
## TEXT RECOGNITION
K
Khanh Tran 已提交
2

W
WenmuZhou 已提交
3 4 5 6 7
- [1 DATA PREPARATION](#DATA_PREPARATION)
    - [1.1 Costom Dataset](#Costom_Dataset)
    - [1.2 Dataset Download](#Dataset_download)
    - [1.3 Dictionary](#Dictionary)  
    - [1.4 Add Space Category](#Add_space_category)
W
WenmuZhou 已提交
8

W
WenmuZhou 已提交
9 10 11 12
- [2 TRAINING](#TRAINING)
    - [2.1 Data Augmentation](#Data_Augmentation)
    - [2.2 Training](#Training)
    - [2.3 Multi-language](#Multi_language)
W
WenmuZhou 已提交
13

W
WenmuZhou 已提交
14
- [3 EVALUATION](#EVALUATION)
W
WenmuZhou 已提交
15

W
WenmuZhou 已提交
16 17
- [4 PREDICTION](#PREDICTION)
    - [4.1 Training engine prediction](#Training_engine_prediction)
W
WenmuZhou 已提交
18 19

<a name="DATA_PREPARATION"></a>
X
xxxpsyduck 已提交
20
### DATA PREPARATION
K
Khanh Tran 已提交
21 22


W
WenmuZhou 已提交
23 24 25
PaddleOCR supports two data formats:
- `LMDB` is used to train data sets stored in lmdb format;
- `general data` is used to train data sets stored in text files:
K
Khanh Tran 已提交
26 27 28 29 30 31

Please organize the dataset as follows:

The default storage path for training data is `PaddleOCR/train_data`, if you already have a dataset on your disk, just create a soft link to the dataset directory:

```
W
WenmuZhou 已提交
32
# linux and mac os
33
ln -sf <path/to/dataset> <path/to/paddle_ocr>/train_data/dataset
W
WenmuZhou 已提交
34 35
# windows
mklink /d <path/to/paddle_ocr>/train_data/dataset <path/to/dataset>
K
Khanh Tran 已提交
36 37
```

W
WenmuZhou 已提交
38
<a name="Costom_Dataset"></a>
W
WenmuZhou 已提交
39
#### 1.1 Costom dataset
K
Khanh Tran 已提交
40 41 42 43 44

If you want to use your own data for training, please refer to the following to organize your data.

- Training set

W
WenmuZhou 已提交
45
It is recommended to put the training images in the same folder, and use a txt file (rec_gt_train.txt) to store the image path and label. The contents of the txt file are as follows:
K
Khanh Tran 已提交
46 47 48 49 50 51

* Note: by default, the image path and image label are split with \t, if you use other methods to split, it will cause training error

```
" Image file name           Image annotation "

W
WenmuZhou 已提交
52 53
train_data/rec/train/word_001.jpg   简单可依赖
train_data/rec/train/word_002.jpg   用科技让复杂的世界更简单
W
WenmuZhou 已提交
54
...
K
Khanh Tran 已提交
55 56 57 58 59 60
```

The final training set should have the following file structure:

```
|-train_data
W
WenmuZhou 已提交
61
  |-rec
W
WenmuZhou 已提交
62 63 64 65 66 67
    |- rec_gt_train.txt
    |- train
        |- word_001.png
        |- word_002.jpg
        |- word_003.jpg
        | ...
K
Khanh Tran 已提交
68 69 70 71 72 73 74 75
```

- Test set

Similar to the training set, the test set also needs to be provided a folder containing all images (test) and a rec_gt_test.txt. The structure of the test set is as follows:

```
|-train_data
W
WenmuZhou 已提交
76
  |-rec
K
Khanh Tran 已提交
77 78 79 80 81 82 83 84
    |-ic15_data
        |- rec_gt_test.txt
        |- test
            |- word_001.jpg
            |- word_002.jpg
            |- word_003.jpg
            | ...
```
W
WenmuZhou 已提交
85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101

<a name="Dataset_download"></a>
#### 1.2 Dataset download

If you do not have a dataset locally, you can download it on the official website [icdar2015](http://rrc.cvc.uab.es/?ch=4&com=downloads). Also refer to [DTRB](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here) ,download the lmdb format dataset required for benchmark

If you want to reproduce the paper indicators of SRN, you need to download offline [augmented data](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA), extraction code: y3ry. The augmented data is obtained by rotation and perturbation of mjsynth and synthtext. Please unzip the data to {your_path}/PaddleOCR/train_data/data_lmdb_Release/training/path.

PaddleOCR provides label files for training the icdar2015 dataset, which can be downloaded in the following ways:

```
# Training set label
wget -P ./train_data/ic15_data  https://paddleocr.bj.bcebos.com/dataset/rec_gt_train.txt
# Test Set Label
wget -P ./train_data/ic15_data  https://paddleocr.bj.bcebos.com/dataset/rec_gt_test.txt
```

W
WenmuZhou 已提交
102
<a name="Dictionary"></a>
W
WenmuZhou 已提交
103
#### 1.3 Dictionary
K
Khanh Tran 已提交
104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119

Finally, a dictionary ({word_dict_name}.txt) needs to be provided so that when the model is trained, all the characters that appear can be mapped to the dictionary index.

Therefore, the dictionary needs to contain all the characters that you want to be recognized correctly. {word_dict_name}.txt needs to be written in the following format and saved in the `utf-8` encoding format:

```
l
d
a
d
r
n
```

In `word_dict.txt`, there is a single word in each line, which maps characters and numeric indexes together, e.g "and" will be mapped to [2 5 1]

W
WenmuZhou 已提交
120 121
PaddleOCR has built-in dictionaries, which can be used on demand.

K
Khanh Tran 已提交
122 123
`ppocr/utils/ppocr_keys_v1.txt` is a Chinese dictionary with 6623 characters.

W
WenmuZhou 已提交
124 125 126 127
`ppocr/utils/ic15_dict.txt` is an English dictionary with 63 characters

`ppocr/utils/dict/french_dict.txt` is a French dictionary with 118 characters

128
`ppocr/utils/dict/japan_dict.txt` is a Japanese dictionary with 4399 characters
W
WenmuZhou 已提交
129

T
tink2123 已提交
130
`ppocr/utils/dict/korean_dict.txt` is a Korean dictionary with 3636 characters
W
WenmuZhou 已提交
131

T
tink2123 已提交
132 133 134
`ppocr/utils/dict/german_dict.txt` is a German dictionary with 131 characters

`ppocr/utils/dict/en_dict.txt` is a English dictionary with 63 characters
W
WenmuZhou 已提交
135

X
xiaoting 已提交
136

W
WenmuZhou 已提交
137
The current multi-language model is still in the demo stage and will continue to optimize the model and add languages. **You are very welcome to provide us with dictionaries and fonts in other languages**,
littletomatodonkey's avatar
fix doc  
littletomatodonkey 已提交
138
If you like, you can submit the dictionary file to [dict](../../ppocr/utils/dict) and we will thank you in the Repo.
K
Khanh Tran 已提交
139 140 141 142


To customize the dict file, please modify the `character_dict_path` field in `configs/rec/rec_icdar15_train.yml` and set `character_type` to `ch`.

T
tink2123 已提交
143 144 145 146
- Custom dictionary

If you need to customize dic file, please add character_dict_path field in configs/rec/rec_icdar15_train.yml to point to your dictionary path. And set character_type to ch.

W
WenmuZhou 已提交
147
<a name="Add_space_category"></a>
W
WenmuZhou 已提交
148
#### 1.4 Add space category
T
tink2123 已提交
149

150
If you want to support the recognition of the `space` category, please set the `use_space_char` field in the yml file to `True`.
T
tink2123 已提交
151 152 153

**Note: use_space_char only takes effect when character_type=ch**

W
WenmuZhou 已提交
154
<a name="TRAINING"></a>
W
WenmuZhou 已提交
155
### 2 TRAINING
K
Khanh Tran 已提交
156 157 158 159 160 161 162 163

PaddleOCR provides training scripts, evaluation scripts, and prediction scripts. In this section, the CRNN recognition model will be used as an example:

First download the pretrain model, you can download the trained model to finetune on the icdar2015 data:

```
cd PaddleOCR/
# Download the pre-trained model of MobileNetV3
T
tink2123 已提交
164
wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar
K
Khanh Tran 已提交
165 166
# Decompress model parameters
cd pretrain_models
T
tink2123 已提交
167
tar -xf rec_mv3_none_bilstm_ctc_v2.0_train.tar && rm -rf rec_mv3_none_bilstm_ctc_v2.0_train.tar
K
Khanh Tran 已提交
168 169 170 171 172
```

Start training:

```
173
# GPU training Support single card and multi-card training, specify the card number through --gpus
T
tink2123 已提交
174
# Training icdar15 English data and The training log will be automatically saved as train.log under "{save_model_dir}"
175
python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c configs/rec/rec_icdar15_train.yml
K
Khanh Tran 已提交
176
```
W
WenmuZhou 已提交
177
<a name="Data_Augmentation"></a>
W
WenmuZhou 已提交
178
#### 2.1 Data Augmentation
T
tink2123 已提交
179 180 181 182 183 184 185

PaddleOCR provides a variety of data augmentation methods. If you want to add disturbance during training, please set `distort: true` in the configuration file.

The default perturbation methods are: cvtColor, blur, jitter, Gasuss noise, random crop, perspective, color reverse.

Each disturbance method is selected with a 50% probability during the training process. For specific code implementation, please refer to: [img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py)

W
WenmuZhou 已提交
186
<a name="Training"></a>
W
WenmuZhou 已提交
187
#### 2.2 Training
T
tink2123 已提交
188

K
Khanh Tran 已提交
189 190 191 192 193 194 195 196 197
PaddleOCR supports alternating training and evaluation. You can modify `eval_batch_step` in `configs/rec/rec_icdar15_train.yml` to set the evaluation frequency. By default, it is evaluated every 500 iter and the best acc model is saved under `output/rec_CRNN/best_accuracy` during the evaluation process.

If the evaluation set is large, the test will be time-consuming. It is recommended to reduce the number of evaluations, or evaluate after training.

* Tip: You can use the `-c` parameter to select multiple model configurations under the `configs/rec/` path for training. The recognition algorithms supported by PaddleOCR are:


| Configuration file |  Algorithm |   backbone |   trans   |   seq      |     pred     |
| :--------: |  :-------:   | :-------:  |   :-------:   |   :-----:   |  :-----:   |
198 199
| [rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml) |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  |
| [rec_chinese_common_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml) |  CRNN | ResNet34_vd |  None   |  BiLSTM |  ctc  |
K
Khanh Tran 已提交
200
| rec_chinese_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  |
W
WenmuZhou 已提交
201
| rec_chinese_common_train.yml |  CRNN |   ResNet34_vd |  None   |  BiLSTM |  ctc  |
K
Khanh Tran 已提交
202 203 204 205 206
| rec_icdar15_train.yml |  CRNN |   Mobilenet_v3 large 0.5 |  None   |  BiLSTM |  ctc  |
| rec_mv3_none_bilstm_ctc.yml |  CRNN |   Mobilenet_v3 large 0.5 |  None   |  BiLSTM |  ctc  |
| rec_mv3_none_none_ctc.yml |  Rosetta |   Mobilenet_v3 large 0.5 |  None   |  None |  ctc  |
| rec_r34_vd_none_bilstm_ctc.yml |  CRNN |   Resnet34_vd |  None   |  BiLSTM |  ctc  |
| rec_r34_vd_none_none_ctc.yml |  Rosetta |   Resnet34_vd |  None   |  None |  ctc  |
L
LDOUBLEV 已提交
207 208
| rec_mv3_tps_bilstm_att.yml |  CRNN |   Mobilenet_v3 |  TPS   |  BiLSTM |  att  |
| rec_r34_vd_tps_bilstm_att.yml |  CRNN |   Resnet34_vd |  TPS   |  BiLSTM |  att  |
T
tink2123 已提交
209
| rec_r50fpn_vd_none_srn.yml    | SRN | Resnet50_fpn_vd    | None    | rnn | srn |
K
Khanh Tran 已提交
210 211


W
WenmuZhou 已提交
212
For training Chinese data, it is recommended to use
213
[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml). If you want to try the result of other algorithms on the Chinese data set, please refer to the following instructions to modify the configuration file:
K
Khanh Tran 已提交
214
co
215
Take `rec_chinese_lite_train_v2.0.yml` as an example:
K
Khanh Tran 已提交
216 217 218
```
Global:
  ...
219 220
  # Add a custom dictionary, such as modify the dictionary, please point the path to the new dictionary
  character_dict_path: ppocr/utils/ppocr_keys_v1.txt
K
Khanh Tran 已提交
221 222 223
  # Modify character type
  character_type: ch
  ...
224
  # Whether to recognize spaces
225
  use_space_char: True
K
Khanh Tran 已提交
226

227 228 229 230

Optimizer:
  ...
  # Add learning rate decay strategy
231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275
  lr:
    name: Cosine
    learning_rate: 0.001
  ...

...

Train:
  dataset:
    # Type of dataset,we support LMDBDateSet and SimpleDataSet
    name: SimpleDataSet
    # Path of dataset
    data_dir: ./train_data/
    # Path of train list
    label_file_list: ["./train_data/train_list.txt"]
    transforms:
      ...
      - RecResizeImg:
          # Modify image_shape to fit long text
          image_shape: [3, 32, 320]
      ...
  loader:
    ...
    # Train batch_size for Single card
    batch_size_per_card: 256
    ...

Eval:
  dataset:
    # Type of dataset,we support LMDBDateSet and SimpleDataSet
    name: SimpleDataSet
    # Path of dataset
    data_dir: ./train_data
    # Path of eval list
    label_file_list: ["./train_data/val_list.txt"]
    transforms:
      ...
      - RecResizeImg:
          # Modify image_shape to fit long text
          image_shape: [3, 32, 320]
      ...
  loader:
    # Eval batch_size for Single card
    batch_size_per_card: 256
    ...
K
Khanh Tran 已提交
276 277 278
```
**Note that the configuration file for prediction/evaluation must be consistent with the training.**

W
WenmuZhou 已提交
279
<a name="Multi_language"></a>
W
WenmuZhou 已提交
280
#### 2.3 Multi-language
W
WenmuZhou 已提交
281

T
tink2123 已提交
282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319
PaddleOCR currently supports 26 (except Chinese) language recognition. A multi-language configuration file template is
provided under the path `configs/rec/multi_languages`: [rec_multi_language_lite_train.yml](../../configs/rec/multi_language/rec_multi_language_lite_train.yml)

There are two ways to create the required configuration file::

1. Automatically generated by script

[generate_multi_language_configs.py](../../configs/rec/multi_language/generate_multi_language_configs.py) Can help you generate configuration files for multi-language models

- Take Italian as an example, if your data is prepared in the following format:
    ```
    |-train_data
        |- it_train.txt # train_set label
        |- it_val.txt # val_set label
        |- data
            |- word_001.jpg
            |- word_002.jpg
            |- word_003.jpg
            | ...
    ```

    You can use the default parameters to generate a configuration file:

    ```bash
    # The code needs to be run in the specified directory
    cd PaddleOCR/configs/rec/multi_language/
    # Set the configuration file of the language to be generated through the -l or --language parameter.
    # This command will write the default parameters into the configuration file
    python3 generate_multi_language_configs.py -l it
    ```

- If your data is placed in another location, or you want to use your own dictionary, you can generate the configuration file by specifying the relevant parameters:

    ```bash
    # -l or --language field is required
    # --train to modify the training set
    # --val to modify the validation set
    # --data_dir to modify the data set directory
T
tink2123 已提交
320
    # --dict to modify the dict path
T
tink2123 已提交
321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362
    # -o to modify the corresponding default parameters
    cd PaddleOCR/configs/rec/multi_language/
    python3 generate_multi_language_configs.py -l it \  # language
    --train {path/of/train_label.txt} \ # path of train_label
    --val {path/of/val_label.txt} \     # path of val_label
    --data_dir {train_data/path} \      # root directory of training data
    --dict {path/of/dict} \             # path of dict
    -o Global.use_gpu=False             # whether to use gpu
    ...

    ```

2. Manually modify the configuration file

   You can also manually modify the following fields in the template:

   ```
    Global:
      use_gpu: True
      epoch_num: 500
      ...
      character_type: it  # language
      character_dict_path:  {path/of/dict} # path of dict

   Train:
      dataset:
        name: SimpleDataSet
        data_dir: train_data/ # root directory of training data
        label_file_list: ["./train_data/train_list.txt"] # train label path
      ...

   Eval:
      dataset:
        name: SimpleDataSet
        data_dir: train_data/ # root directory of val data
        label_file_list: ["./train_data/val_list.txt"] # val label path
      ...

   ```

Currently, the multi-language algorithms supported by PaddleOCR are:

T
tink2123 已提交
363
| Configuration file |  Algorithm name |   backbone |   trans   |   seq      |     pred     |  language | character_type |
T
tink2123 已提交
364 365
| :--------: |  :-------:   | :-------:  |   :-------:   |   :-----:   |  :-----:   | :-----:  | :-----:  |
| rec_chinese_cht_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | chinese traditional  | chinese_cht|
T
tink2123 已提交
366
| rec_en_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | English(Case sensitive)   | EN |
T
tink2123 已提交
367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391
| rec_french_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | French |  french |
| rec_ger_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | German   | german |
| rec_japan_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Japanese | japan |
| rec_korean_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Korean  | korean |
| rec_it_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Italian  | it |
| rec_xi_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Spanish |  xi |
| rec_pu_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Portuguese   | pu |
| rec_ru_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Russia  | ru |
| rec_ar_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Arabic  | ar |
| rec_hi_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Hindi |  hi |
| rec_ug_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Uyghur  | ug |
| rec_fa_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Persian(Farsi)  | fa |
| rec_ur_ite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Urdu  | ur |
| rec_rs_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Serbian(latin) | rs |
| rec_oc_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Occitan  | oc |
| rec_mr_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Marathi  | mr |
| rec_ne_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Nepali  | ne |
| rec_rsc_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Serbian(cyrillic) |  rsc |
| rec_bg_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Bulgarian  | bg |
| rec_uk_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Ukranian  | uk |
| rec_be_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Belarusian   | be |
| rec_te_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Telugu  | te |
| rec_ka_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Kannada  | ka |
| rec_ta_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Tamil |  ta |

W
WenmuZhou 已提交
392 393 394 395 396 397 398 399 400 401

The multi-language model training method is the same as the Chinese model. The training data set is 100w synthetic data. A small amount of fonts and test data can be downloaded on [Baidu Netdisk](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA),Extraction code:frgi.

If you want to finetune on the basis of the existing model effect, please refer to the following instructions to modify the configuration file:

Take `rec_french_lite_train` as an example:

```
Global:
  ...
402
  # Add a custom dictionary, such as modify the dictionary, please point the path to the new dictionary
W
WenmuZhou 已提交
403 404
  character_dict_path: ./ppocr/utils/dict/french_dict.txt
  ...
405
  # Whether to recognize spaces
406
  use_space_char: True
407

W
WenmuZhou 已提交
408
...
409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428

Train:
  dataset:
    # Type of dataset,we support LMDBDateSet and SimpleDataSet
    name: SimpleDataSet
    # Path of dataset
    data_dir: ./train_data/
    # Path of train list
    label_file_list: ["./train_data/french_train.txt"]
    ...

Eval:
  dataset:
    # Type of dataset,we support LMDBDateSet and SimpleDataSet
    name: SimpleDataSet
    # Path of dataset
    data_dir: ./train_data
    # Path of eval list
    label_file_list: ["./train_data/french_val.txt"]
    ...
W
WenmuZhou 已提交
429
```
K
Khanh Tran 已提交
430

W
WenmuZhou 已提交
431
<a name="EVALUATION"></a>
W
WenmuZhou 已提交
432
### 3 EVALUATION
K
Khanh Tran 已提交
433

W
WenmuZhou 已提交
434
The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/rec/rec_icdar15_train.yml` file.
K
Khanh Tran 已提交
435 436 437

```
# GPU evaluation, Global.checkpoints is the weight to be tested
W
WenmuZhou 已提交
438
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy
K
Khanh Tran 已提交
439 440
```

W
WenmuZhou 已提交
441
<a name="PREDICTION"></a>
W
WenmuZhou 已提交
442
### 4 PREDICTION
K
Khanh Tran 已提交
443

W
WenmuZhou 已提交
444
<a name="Training_engine_prediction"></a>
W
WenmuZhou 已提交
445
#### 4.1 Training engine prediction
K
Khanh Tran 已提交
446 447 448 449 450 451 452

Using the model trained by paddleocr, you can quickly get prediction through the following script.

The default prediction picture is stored in `infer_img`, and the weight is specified via `-o Global.checkpoints`:

```
# Predict English results
W
WenmuZhou 已提交
453
python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words/en/word_1.jpg
K
Khanh Tran 已提交
454 455 456 457
```

Input image:

458
![](../imgs_words/en/word_1.png)
K
Khanh Tran 已提交
459 460 461 462 463

Get the prediction result of the input image:

```
infer_img: doc/imgs_words/en/word_1.png
T
tink2123 已提交
464
        result: ('joint', 0.9998967)
K
Khanh Tran 已提交
465 466
```

467
The configuration file used for prediction must be consistent with the training. For example, you completed the training of the Chinese model with `python3 tools/train.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml`, you can use the following command to predict the Chinese model:
K
Khanh Tran 已提交
468 469 470

```
# Predict Chinese results
W
WenmuZhou 已提交
471
python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words/ch/word_1.jpg
K
Khanh Tran 已提交
472 473 474 475
```

Input image:

476
![](../imgs_words/ch/word_1.jpg)
K
Khanh Tran 已提交
477 478 479 480 481

Get the prediction result of the input image:

```
infer_img: doc/imgs_words/ch/word_1.jpg
T
tink2123 已提交
482
        result: ('韩国小馆', 0.997218)
K
Khanh Tran 已提交
483
```