Gradient-Based Graph Attention for Scene Text Image Super-resolution
Keywords:CV: Low Level & Physics-Based Vision, CV: Image and Video Retrieval, CV: Interpretability and Transparency, CV: Language and Vision, CV: Learning & Optimization for CV, CV: Applications
AbstractScene text image super-resolution (STISR) in the wild has been shown to be beneficial to support improved vision-based text recognition from low-resolution imagery. An intuitive way to enhance STISR performance is to explore the well-structured and repetitive layout characteristics of text and exploit these as prior knowledge to guide model convergence. In this paper, we propose a novel gradient-based graph attention method to embed patch-wise text layout contexts into image feature representations for high-resolution text image reconstruction in an implicit and elegant manner. We introduce a non-local group-wise attention module to extract text features which are then enhanced by a cascaded channel attention module and a novel gradient-based graph attention module in order to obtain more effective representations by exploring correlations of regional and local patch-wise text layout properties. Extensive experiments on the benchmark TextZoom dataset convincingly demonstrate that our method supports excellent text recognition and outperforms the current state-of-the-art in STISR. The source code is available at https://github.com/xyzhu1/TSAN.
How to Cite
Zhu, X., Guo, K., Fang, H., Ding, R., Wu, Z., & Schaefer, G. (2023). Gradient-Based Graph Attention for Scene Text Image Super-resolution. Proceedings of the AAAI Conference on Artificial Intelligence, 37(3), 3861-3869. https://doi.org/10.1609/aaai.v37i3.25499
AAAI Technical Track on Computer Vision III