Recognition-Guided Diffusion Model for Scene Text Image Super-Resolution
Yuxuan Zhou Liangcai Gao Zhi Tang Baole Wei
Scene Text Image Super-Resolution (STISR) aims to enhance the resolution and
legibility of text within low-resolution (LR) images, consequently elevating
recognition accuracy in Scene Text Recognition (STR). Previous methods
predominantly employ discriminative Convolutional Neural Networks (CNNs)
augmented with diverse forms of text guidance to address this issue.
Nevertheless, they remain deficient when confronted with severely blurred
images, due to their insufficient generation capability when little structural
or semantic information can be extracted from original images. Therefore, we
introduce RGDiffSR, a Recognition-Guided Diffusion model for scene text image
Super-Resolution, which exhibits great generative diversity and fidelity even
in challenging scenarios. Moreover, we propose a Recognition-Guided Denoising
Network, to guide the diffusion model generating LR-consistent results through
succinct semantic guidance. Experiments on the TextZoom dataset demonstrate the
superiority of RGDiffSR over prior state-of-the-art methods in both text
recognition accuracy and image fidelity.