OCR model list（V2.0, updated on 2021.1.20）

Note

1. Compared with models 1.1, which are trained with static graph programming paradigm, models 2.0 are the dynamic graph trained version and achieve close performance.
2. All models in this tutorial are all ppocr-series models, for more introduction of algorithms and models based on public dataset, you can refer to algorithm overview tutorial.

• 1. Text Detection Model
• 2. Text Recognition Model
  • Chinese Recognition Model
  • English Recognition Model
  • Multilingual Recognition Model
• 3. Text Angle Classification Model
• 4. Paddle-Lite Model

The downloadable models provided by PaddleOCR include inference model, trained model, pre-trained model and slim model. The differences between the models are as follows:

Relationship of the above models is as follows.

1. Text Detection Model

2. Text Recognition Model

Chinese Recognition Model

Note: The trained model is finetuned on the pre-trained model with real data and synthsized vertical text data, which achieved better performance in real scene. The pre-trained model is directly trained on the full amount of real data and synthsized data, which is more suitable for finetune on your own dataset.

English Recognition Model

Multilingual Recognition Model（Updating...）

Note： The configuration file of the new multi language model is generated by code. You can use the --help parameter to check which multi language are supported by current PaddleOCR.

# The code needs to run in the specified directory
cd {your/path/}PaddleOCR/configs/rec/multi_language/
python3 generate_multi_language_configs.py --help

Take the Italian configuration file as an example：

1.Generate Italian configuration file to test the model provided

you can generate the default configuration file through the following command, and use the default language dictionary provided by paddleocr for prediction.

# The code needs to run in the specified directory
cd {your/path/}PaddleOCR/configs/rec/multi_language/
# Set the required language configuration file through -l or --language parameter
# This command will write the default parameter to the configuration file.
python3 generate_multi_language_configs.py -l it

2. Generate Italian configuration file to train your own data

If you want to train your own model, you can prepare the training set file, verification set file, dictionary file and training data path. Here we assume that the Italian training set, verification set, dictionary and training data path are:

• Training set:{your/path/}PaddleOCR/train_data/train_list.txt
• Validation set: {your/path/}PaddleOCR/train_data/val_list.txt
• Use the default dictionary provided by paddleocr:{your/path/}PaddleOCR/ppocr/utils/dict/it_dict.txt
• Training data path:{your/path/}PaddleOCR/train_data

# The code needs to run in the specified directory
cd {your/path/}PaddleOCR/configs/rec/multi_language/
# The -l or --language parameter is required
# --train modify train_list path
# --val modify eval_list path
# --data_dir modify data dir
# -o modify default parameters
# --dict Change the dictionary path. The example uses the default dictionary path, so that this parameter can be empty.
python3 generate_multi_language_configs.py -l it \
--train {path/to/train_list} \
--val {path/to/val_list} \
--data_dir {path/to/data_dir} \
-o Global.use_gpu=False

For more supported languages, please refer to : Multi-language model

3. Text Angle Classification Model

4. Paddle-Lite Model