HiREN: Towards Higher Supervision Quality for Better Scene Text Image
  Super-Resolution
Minyi Zhao Yi Xu Bingjia Li Jie Wang Jihong Guan Shuigeng Zhou
Scene text image super-resolution (STISR) is an important pre-processing
technique for text recognition from low-resolution scene images. Nowadays,
various methods have been proposed to extract text-specific information from
high-resolution (HR) images to supervise STISR model training. However, due to
uncontrollable factors (e.g. shooting equipment, focus, and environment) in
manually photographing HR images, the quality of HR images cannot be
guaranteed, which unavoidably impacts STISR performance. Observing the quality
issue of HR images, in this paper we propose a novel idea to boost STISR by
first enhancing the quality of HR images and then using the enhanced HR images
as supervision to do STISR. Concretely, we develop a new STISR framework,
called High-Resolution ENhancement (HiREN) that consists of two branches and a
quality estimation module. The first branch is developed to recover the
low-resolution (LR) images, and the other is an HR quality enhancement branch
aiming at generating high-quality (HQ) text images based on the HR images to
provide more accurate supervision to the LR images. As the degradation from HQ
to HR may be diverse, and there is no pixel-level supervision for HQ image
generation, we design a kernel-guided enhancement network to handle various
degradation, and exploit the feedback from a recognizer and text-level
annotations as weak supervision signal to train the HR enhancement branch.
Then, a quality estimation module is employed to evaluate the qualities of HQ
images, which are used to suppress the erroneous supervision information by
weighting the loss of each image. Extensive experiments on TextZoom show that
HiREN can work well with most existing STISR methods and significantly boost
their performances.