SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition¶
1. Introduction¶
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 |
2. Environment¶
Please refer to "Environment Preparation" to configure the PaddleOCR environment, and refer to "Project Clone"to clone the project code.
3. Model Training / Evaluation / Prediction¶
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:
Evaluation¶
Prediction¶
4. Inference and Deployment¶
4.1 Python Inference¶
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:
For SPIN text recognition model inference, the following commands can be executed:
4.2 C++ Inference¶
Not supported
4.3 Serving¶
Not supported
4.4 More¶
Not supported
5. FAQ¶
Citation¶
@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},
}