  Scene Text Image Super-resolution (STISR) aims to recover high-resolution
(HR) scene text images with visually pleasant and readable text content from
the given low-resolution (LR) input. Most existing works focus on recovering
English texts, which have relatively simple character structures, while little
work has been done on the more challenging Chinese texts with diverse and
complex character structures. In this paper, we propose a real-world
Chinese-English benchmark dataset, namely Real-CE, for the task of STISR with
the emphasis on restoring structurally complex Chinese characters. The
benchmark provides 1,935/783 real-world LR-HR text image pairs~(contains 33,789
text lines in total) for training/testing in 2$\times$ and 4$\times$ zooming
modes, complemented by detailed annotations, including detection boxes and text
transcripts. Moreover, we design an edge-aware learning method, which provides
structural supervision in image and feature domains, to effectively reconstruct
the dense structures of Chinese characters. We conduct experiments on the
proposed Real-CE benchmark and evaluate the existing STISR models with and
without our edge-aware loss. The benchmark, including data and source code, is
available at https://github.com/mjq11302010044/Real-CE.
