A Benchmark for Chinese-English Scene Text Image Super-resolution
Jianqi Ma, Zhetong Liang, Wangmeng Xiang, Xi Yang, Lei Zhang

TL;DR
This paper introduces a new Chinese-English scene text image super-resolution benchmark dataset, Real-CE, with a focus on restoring complex Chinese characters, and proposes an edge-aware learning method to improve reconstruction quality.
Contribution
The paper presents the first real-world Chinese-English STISR benchmark dataset and an edge-aware learning approach tailored for complex Chinese characters.
Findings
Edge-aware loss improves reconstruction of Chinese characters
Benchmark dataset enables evaluation of STISR models on Chinese texts
Proposed method outperforms existing models on Real-CE
Abstract
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 and 4 zooming modes, complemented by detailed annotations, including detection boxes and text transcripts. Moreover, we design an edge-aware learning method,…
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Code & Models
Videos
A Benchmark for Chinese-English Scene Text Image Super-Resolution· youtube
Taxonomy
TopicsAdvanced Image Processing Techniques · Digital Media Forensic Detection · Ideological and Political Education
MethodsFocus
