  Scene text recognition has been a hot topic in computer vision. Recent
methods adopt the attention mechanism for sequence prediction which achieve
convincing results. However, we argue that the existing attention mechanism
faces the problem of attention diffusion, in which the model may not focus on a
certain character area. In this paper, we propose Gaussian Constrained
Attention Network to deal with this problem. It is a 2D attention-based method
integrated with a novel Gaussian Constrained Refinement Module, which predicts
an additional Gaussian mask to refine the attention weights. Different from
adopting an additional supervision on the attention weights simply, our
proposed method introduces an explicit refinement. In this way, the attention
weights will be more concentrated and the attention-based recognition network
achieves better performance. The proposed Gaussian Constrained Refinement
Module is flexible and can be applied to existing attention-based methods
directly. The experiments on several benchmark datasets demonstrate the
effectiveness of our proposed method. Our code has been available at
https://github.com/Pay20Y/GCAN.
