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。