GTC: Guided Training of CTC Towards Efficient and Accurate Scene Text Recognition

• URL: http://arxiv.org/abs/2002.01276v1
• Date: Tue, 4 Feb 2020 13:26:14 GMT
• Title: GTC: Guided Training of CTC Towards Efficient and Accurate Scene Text Recognition
• Authors: Wenyang Hu, Xiaocong Cai, Jun Hou, Shuai Yi, Zhiping Lin
• Abstract summary: We propose the guided training of CTC model, where CTC model learns a better alignment and feature representations from a more powerful attentional guidance. With the benefit of guided training, CTC model achieves robust and accurate prediction for both regular and irregular scene text. To further leverage the potential of CTC decoder, a graph convolutional network (GCN) is proposed to learn the local correlations of extracted features.
• Score: 27.38969404322089
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Connectionist Temporal Classification (CTC) and attention mechanism are two main approaches used in recent scene text recognition works. Compared with attention-based methods, CTC decoder has a much shorter inference time, yet a lower accuracy. To design an efficient and effective model, we propose the guided training of CTC (GTC), where CTC model learns a better alignment and feature representations from a more powerful attentional guidance. With the benefit of guided training, CTC model achieves robust and accurate prediction for both regular and irregular scene text while maintaining a fast inference speed. Moreover, to further leverage the potential of CTC decoder, a graph convolutional network (GCN) is proposed to learn the local correlations of extracted features. Extensive experiments on standard benchmarks demonstrate that our end-to-end model achieves a new state-of-the-art for regular and irregular scene text recognition and needs 6 times shorter inference time than attentionbased methods.

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