Scene text image super-resolution (STISR), aiming to improve image quality
while boosting downstream scene text recognition accuracy, has recently
achieved great success. However, most existing methods treat the foreground
(character regions) and background (non-character regions) equally in the
forward process, and neglect the disturbance from the complex background, thus
limiting the performance. To address these issues, in this paper, we propose a
novel method LEMMA that explicitly models character regions to produce
high-level text-specific guidance for super-resolution. To model the location
of characters effectively, we propose the location enhancement module to
extract character region features based on the attention map sequence. Besides,
we propose the multi-modal alignment module to perform bidirectional
visual-semantic alignment to generate high-quality prior guidance, which is
then incorporated into the super-resolution branch in an adaptive manner using
the proposed adaptive fusion module. Experiments on TextZoom and four scene
text recognition benchmarks demonstrate the superiority of our method over
other state-of-the-art methods. Code is available at
this https URL

本研究提出了一种名称为 LEMMA 的新方法，通过显式建模字符区域，生成具有高级文本特定引导的超分辨率图像，通过位置增强模块和多模态对齐模块提升字符区域的特征提取和视觉 - 语义对齐，并使用自适应融合模块将先验引导无缝融合到超分辨率分支中。在 TextZoom 和四个场景文本识别基准测试上的实验证明了本方法相对于其他最先进技术的优越性。