  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
https://github.com/csguoh/LEMMA.
