Paper:
SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition Chengwei Zhang, Yunlu Xu, Zhanzhan Cheng, Shiliang Pu, Yi Niu, Fei Wu, Futai Zou AAAI, 2020
Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets. The algorithm reproduction effect is as follows:
Model | Backbone | config | Acc | Download link |
---|---|---|---|---|
SPIN | ResNet32 | rec_r32_gaspin_bilstm_att.yml | 90.00% | trained model |
Please refer to "Environment Preparation" to configure the PaddleOCR environment, and refer to "Project Clone" to clone the project code.
Please refer to Text Recognition Tutorial. PaddleOCR modularizes the code, and training different recognition models only requires changing the configuration file.
Training:
Specifically, after the data preparation is completed, the training can be started. The training command is as follows:
#Single GPU training (long training period, not recommended)
python3 tools/train.py -c configs/rec/rec_r32_gaspin_bilstm_att.yml
#Multi GPU training, specify the gpu number through the --gpus parameter
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/rec/rec_r32_gaspin_bilstm_att.yml
Evaluation:
# GPU evaluation
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_r32_gaspin_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
Prediction:
# The configuration file used for prediction must match the training
python3 tools/infer_rec.py -c configs/rec/rec_r32_gaspin_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
First, the model saved during the SPIN text recognition training process is converted into an inference model. you can use the following command to convert:
python3 tools/export_model.py -c configs/rec/rec_r32_gaspin_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.save_inference_dir=./inference/rec_r32_gaspin_bilstm_att
For SPIN text recognition model inference, the following commands can be executed:
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./inference/rec_r32_gaspin_bilstm_att/" --rec_image_shape="3, 32, 100" --rec_algorithm="SPIN" --rec_char_dict_path="/ppocr/utils/dict/spin_dict.txt" --use_space_char=False
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@article{2020SPIN,
title={SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition},
author={Chengwei Zhang and Yunlu Xu and Zhanzhan Cheng and Shiliang Pu and Yi Niu and Fei Wu and Futai Zou},
journal={AAAI2020},
year={2020},
}
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