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/` 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:
PaddleOCR also provides multi-language. The configuration file in `configs/rec/multi_languages` provides multi-language configuration files. Currently, the multi-language algorithms supported by PaddleOCR are:
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)。
| Configuration file | Algorithm name | backbone | trans | seq | pred | language ｜
There are two ways to create the required configuration file:：
| rec_en_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | English ｜
| rec_french_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | French ｜
1. Automatically generated by script
| rec_ger_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | German ｜
| rec_japan_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Japanese ｜
[generate_multi_language_configs.py](../../configs/rec/multi_language/generate_multi_language_configs.py) Can help you generate configuration files for multi-language models
| rec_korean_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Korean ｜
- 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
# --dict to modify the dict path
# -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
| 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 |
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.
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.
