Scene Text Image Super-resolution (STISR) has recently achieved great success
as a preprocessing method for scene text recognition. STISR aims to transform
blurred and noisy low-resolution (LR) text images in real-world settings into
clear high-resolution (HR) text images suitable for scene text recognition. In
this study, we leverage text-conditional diffusion models (DMs), known for
their impressive text-to-image synthesis capabilities, for STISR tasks. Our
experimental results revealed that text-conditional DMs notably surpass
existing STISR methods. Especially when texts from LR text images are given as
input, the text-conditional DMs are able to produce superior quality
super-resolution text images. Utilizing this capability, we propose a novel
framework for synthesizing LR-HR paired text image datasets. This framework
consists of three specialized text-conditional DMs, each dedicated to text
image synthesis, super-resolution, and image degradation. These three modules
are vital for synthesizing distinct LR and HR paired images, which are more
suitable for training STISR methods. Our experiments confirmed that these
synthesized image pairs significantly enhance the performance of STISR methods
in the TextZoom evaluation.

本研究使用文本条件扩散模型（DMs）实现场景文本图像超分辨率（STISR）任务，并提出了一个针对合成低分辨率 - 高分辨率配对文本图像数据集的新框架，该框架包含三个专门的 DMs 模块，分别用于文本图像合成、超分辨率和图像退化。实验证明，使用这些合成图像对可显著提高 STISR 方法在 TextZoom 评估中的性能。