  Scene text image super-resolution (STISR) has been regarded as an important
pre-processing task for text recognition from low-resolution scene text images.
Most recent approaches use the recognizer's feedback as clues to guide
super-resolution. However, directly using recognition clue has two problems: 1)
Compatibility. It is in the form of probability distribution, has an obvious
modal gap with STISR - a pixel-level task; 2) Inaccuracy. it usually contains
wrong information, thus will mislead the main task and degrade super-resolution
performance. In this paper, we present a novel method C3-STISR that jointly
exploits the recognizer's feedback, visual and linguistical information as
clues to guide super-resolution. Here, visual clue is from the images of texts
predicted by the recognizer, which is informative and more compatible with the
STISR task; while linguistical clue is generated by a pre-trained
character-level language model, which is able to correct the predicted texts.
We design effective extraction and fusion mechanisms for the triple cross-modal
clues to generate a comprehensive and unified guidance for super-resolution.
Extensive experiments on TextZoom show that C3-STISR outperforms the SOTA
methods in fidelity and recognition performance. Code is available in
https://github.com/zhaominyiz/C3-STISR.
