Gaussian Constrained Attention Network for Scene Text Recognition

Abstract

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.

arXiv commentICPR2020

References in corpus (11)

• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition
• Focusing Attention: Towards Accurate Text Recognition in Natural Images
• Scene Text Recognition with Sliding Convolutional Character Models
• Enhancing Energy Minimization Framework for Scene Text Recognition with Top-Down Cues
• 2D-CTC for Scene Text Recognition
• SCAN: Sliding Convolutional Attention Network for Scene Text Recognition
• Attention Optimization for Abstractive Document Summarization
• T-GSA: Transformer with Gaussian-weighted self-attention for speech enhancement
• AdaDNNs: Adaptive Ensemble of Deep Neural Networks for Scene Text Recognition
• Attention Is All You Need for Chinese Word Segmentation

Cited by in corpus (4)

• Rethinking Text Line Recognition Models
• Dense Semantic Contrast for Self-Supervised Visual Representation Learning
• Mask is All You Need: Rethinking Mask R-CNN for Dense and Arbitrary-Shaped Scene Text Detection
• PIMNet: A Parallel, Iterative and Mimicking Network for Scene Text Recognition