Blind text image super-resolution (SR) is challenging as one needs to cope
with diverse font styles and unknown degradation. To address the problem,
existing methods perform character recognition in parallel to regularize the SR
task, either through a loss constraint or intermediate feature condition.
Nonetheless, the high-level prior could still fail when encountering severe
degradation. The problem is further compounded given characters of complex
structures, e.g., Chinese characters that combine multiple pictographic or
ideographic symbols into a single character. In this work, we present a novel
prior that focuses more on the character structure. In particular, we learn to
encapsulate rich and diverse structures in a StyleGAN and exploit such
generative structure priors for restoration. To restrict the generative space
of StyleGAN so that it obeys the structure of characters yet remains flexible
in handling different font styles, we store the discrete features for each
character in a codebook. The code subsequently drives the StyleGAN to generate
high-resolution structural details to aid text SR. Compared to priors based on
character recognition, the proposed structure prior exerts stronger
character-specific guidance to restore faithful and precise strokes of a
designated character. Extensive experiments on synthetic and real datasets
demonstrate the compelling performance of the proposed generative structure
prior in facilitating robust text SR.

通过学习 StyleGAN 的可生成结构，将不同字体风格的离散特征编码嵌入代码库，以提供生成一致的高分辨率与结构细节，比基于字符识别的方法有更高的恢复精度和字符特定的引导作用。

盲文本图像超分辨率的生成结构先验学习

Learning Generative Structure Prior for Blind Text Image  Super-resolution

Text image super-resolution is a unique and important task to enhance
readability of text images to humans. It is widely used as pre-processing in
scene text recognition. However, due to the complex degradation in natural
scenes, recovering high-resolution texts from the low-resolution inputs is
ambiguous and challenging. Existing methods mainly leverage deep neural
networks trained with pixel-wise losses designed for natural image
reconstruction, which ignore the unique character characteristics of texts. A
few works proposed content-based losses. However, they only focus on text
recognizers' accuracy, while the reconstructed images may still be ambiguous to
humans. Further, they often have weak generalizability to handle cross
languages. To this end, we present TATSR, a Text-Aware Text Super-Resolution
framework, which effectively learns the unique text characteristics using
Criss-Cross Transformer Blocks (CCTBs) and a novel Content Perceptual (CP)
Loss. The CCTB extracts vertical and horizontal content information from text
images by two orthogonal transformers, respectively. The CP Loss supervises the
text reconstruction with content semantics by multi-scale text recognition
features, which effectively incorporates content awareness into the framework.
Extensive experiments on various language datasets demonstrate that TATSR
outperforms state-of-the-art methods in terms of both recognition accuracy and
human perception.

使用 Criss-Cross Transformer Blocks 和 Content Perceptual Loss，利用多尺度文本识别特征将内容意义有效地融入框架中，设计出了 TATSR 来提高文本图像的可读性和人类感知。在各种语言数据集上的广泛实验表明，TATSR 在识别精度和人类感知方面优于现有技术。

场景文本图像超分辨率：基于内容感知损失和交叉变换块的方法

Scene Text Image Super-Resolution via Content Perceptual Loss and  Criss-Cross Transformer Blocks

Text image super-resolution is a challenging yet open research problem in the
computer vision community. In particular, low-resolution images hamper the
performance of typical optical character recognition (OCR) systems. In this
article, we summarize our entry to the ICDAR2015 Competition on Text Image
Super-Resolution. Experiments are based on the provided ICDAR2015 TextSR
dataset and the released Tesseract-OCR 3.02 system. We report that our winning
entry of text image super-resolution framework has largely improved the OCR
performance with low-resolution images used as input, reaching an OCR accuracy
score of 77.19%, which is comparable with that of using the original
high-resolution images 78.80%.

本文总结了我们参加 ICDAR2015 文本图像超分辨率比赛的入门知识。根据 ICDAR2015 TextSR 数据集和 Tesseract-OCR 3.02 系统，我们的文本图像超分辨率框架取得了胜利，使用低分辨率图像作为输入，OCR 准确性得分为 77.19％，与使用原始高分辨率图像的 78.80％相当。