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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.

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:

model type model format description
inference model inference.pdmodel、inference.pdiparams Used for inference based on Paddle inference engine，detail
trained model, pre-trained model *.pdparams、*.pdopt、*.states The checkpoints model saved in the training process, which stores the parameters of the model, mostly used for model evaluation and continuous training.
slim model *.nb Model compressed by PaddleSim (a model compression tool using PaddlePaddle), which is suitable for mobile-side deployment scenarios (Paddle-Lite is needed for slim model deployment).

Relationship of the above models is as follows.

1. Text Detection Model

model name description config model size download
ch_ppocr_mobile_slim_v2.0_det Slim pruned lightweight model, supporting Chinese, English, multilingual text detection ch_det_mv3_db_v2.0.yml 2.6M inference model
ch_ppocr_mobile_v2.0_det Original lightweight model, supporting Chinese, English, multilingual text detection ch_det_mv3_db_v2.0.yml 3M inference model / trained model
ch_ppocr_server_v2.0_det General model, which is larger than the lightweight model, but achieved better performance ch_det_res18_db_v2.0.yml 47M inference model / trained model

2. Text Recognition Model

Chinese Recognition Model

model name description config model size download
ch_ppocr_mobile_slim_v2.0_rec Slim pruned and quantized lightweight model, supporting Chinese, English and number recognition rec_chinese_lite_train_v2.0.yml 6M inference model / trained model
ch_ppocr_mobile_v2.0_rec Original lightweight model, supporting Chinese, English and number recognition rec_chinese_lite_train_v2.0.yml 5.2M inference model / trained model / pre-trained model
ch_ppocr_server_v2.0_rec General model, supporting Chinese, English and number recognition rec_chinese_common_train_v2.0.yml 94.8M inference model / trained model / pre-trained 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

model name description config model size download
en_number_mobile_slim_v2.0_rec Slim pruned and quantized lightweight model, supporting English and number recognition rec_en_number_lite_train.yml 2.7M inference model / trained model
en_number_mobile_v2.0_rec Original lightweight model, supporting English and number recognition rec_en_number_lite_train.yml 2.6M inference model / trained 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
model name dict file description config model size download
french_mobile_v2.0_rec ppocr/utils/dict/french_dict.txt Lightweight model for French recognition rec_french_lite_train.yml 2.65M inference model / trained model
german_mobile_v2.0_rec ppocr/utils/dict/german_dict.txt Lightweight model for German recognition rec_german_lite_train.yml 2.65M inference model / trained model
korean_mobile_v2.0_rec ppocr/utils/dict/korean_dict.txt Lightweight model for Korean recognition rec_korean_lite_train.yml 3.9M inference model / trained model
japan_mobile_v2.0_rec ppocr/utils/dict/japan_dict.txt Lightweight model for Japanese recognition rec_japan_lite_train.yml 4.23M inference model / trained model
chinese_cht_mobile_v2.0_rec ppocr/utils/dict/chinese_cht_dict.txt Lightweight model for chinese cht recognition rec_chinese_cht_lite_train.yml 5.63M inference model / trained model
te_mobile_v2.0_rec ppocr/utils/dict/te_dict.txt Lightweight model for Telugu recognition rec_te_lite_train.yml 2.63M inference model / trained model
ka_mobile_v2.0_rec ppocr/utils/dict/ka_dict.txt Lightweight model for Kannada recognition rec_ka_lite_train.yml 2.63M inference model / trained model
ta_mobile_v2.0_rec ppocr/utils/dict/ta_dict.txt Lightweight model for Tamil recognition rec_ta_lite_train.yml 2.63M inference model / trained model
latin_mobile_v2.0_rec ppocr/utils/dict/latin_dict.txt Lightweight model for latin recognition rec_latin_lite_train.yml 2.6M inference model / trained model
arabic_mobile_v2.0_rec ppocr/utils/dict/arabic_dict.txt Lightweight model for arabic recognition rec_arabic_lite_train.yml 2.6M inference model / trained model
cyrillic_mobile_v2.0_rec ppocr/utils/dict/cyrillic_dict.txt Lightweight model for cyrillic recognition rec_cyrillic_lite_train.yml 2.6M inference model / trained model
devanagari_mobile_v2.0_rec ppocr/utils/dict/devanagari_dict.txt Lightweight model for devanagari recognition rec_devanagari_lite_train.yml 2.6M inference model / trained model

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

3. Text Angle Classification Model

model name description config model size download
ch_ppocr_mobile_slim_v2.0_cls Slim quantized model cls_mv3.yml 2.1M inference model / trained model
ch_ppocr_mobile_v2.0_cls Original model cls_mv3.yml 1.38M inference model / trained model

4. Paddle-Lite Model

Version Introduction Model size Detection model Text Direction model Recognition model Paddle-Lite branch
V2.0 extra-lightweight chinese OCR optimized model 7.8M download link download link download link v2.9
V2.0(slim) extra-lightweight chinese OCR optimized model 3.3M download link download link download link v2.9