Abstract
Scene text image super-resolution (STISR) enhances the resolution and quality of low-resolution images. Unlike previous studies that treated scene text images as natural images, recent methods using a text prior (TP), extracted from a pre-trained text recognizer, have shown strong performance. However, two major issues emerge: (1) Explicit categorical priors, like TP, can negatively impact STISR if incorrect. We reveal that these explicit priors are unstable and propose replacing them with Non-CAtegorical Prior (NCAP) using penultimate layer representations. (2) Pre-trained recognizers used to generate TP struggle with low-resolution images. To address this, most studies jointly train the recognizer with the STISR network to bridge the domain gap between low- and high-resolution images, but this can cause an overconfidence phenomenon in the prior modality. We highlight this issue and propose a method to mitigate it by mixing hard and soft labels. Experiments on the TextZoom dataset demonstrate an improvement by 3.5%, while our method significantly enhances generalization performance by 14.8\% across four text recognition datasets. Our method generalizes to all TP-guided STISR networks.
Abstract (translated)
场景文本图像超分辨率（STISR）技术旨在提高低分辨率图像的分辨率和质量。与以往将场景文本图像视为自然图像处理的研究不同，最近使用从预训练的文字识别器中提取的文本先验（TP）的方法展现了强大的性能。然而，这种方法存在两个主要问题： 1. 显式的类别先验，如TP，在错误的情况下会负面影响STISR的效果。我们发现这些显式先验不稳定，并提出用来自倒数第二层表示的非分类前缀（NCAP）来替代它们。 2. 用于生成TP的预训练识别器在处理低分辨率图像时遇到困难。为了应对这一问题，大多数研究通过将识别器与STISR网络联合训练来弥合低分辨率和高分辨率图像之间的领域差距，但这可能会导致先验模态中的过度自信现象。我们强调了这个问题，并提出了一种混合硬标签和软标签的方法来缓解它。 在TextZoom数据集上的实验表明，我们的方法相比基准提高了3.5%的性能，而在四个文本识别数据集中，我们的方法显著增强了泛化性能，增幅达14.8%。此外，我们提出的方法能够应用于所有基于TP引导的STISR网络中。
URL
https://arxiv.org/abs/2504.00410
