Diffusion models have gained increasing attention for their impressive generation abilities
but currently struggle with rendering accurate and coherent text. To address this issue, we …

Scene text image super-resolution aims to increase the resolution and readability of the text
in low-resolution images. Though significant improvement has been achieved by deep …

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 …

Existing text recognition methods usually need large-scale training data. Most of them rely
on synthetic training data due to the lack of annotated real images. However, there is a …

Scene text recognition has been studied for decades due to its broad applications. However,
despite Chinese characters possessing different characteristics from Latin characters, such …

Scene text image super-resolution (STISR) aims to improve the resolution and visual quality
of low-resolution (LR) scene text images, while simultaneously boost the performance of text …

The flourishing blossom of deep learning has witnessed the rapid development of text
recognition in recent years. However, the existing text recognition methods are mainly …

In the last decade, the blossom of deep learning has witnessed the rapid development of
scene text recognition. However, the recognition of low-resolution scene text images …

Text images contain both visual and linguistic information. However, existing pre-training
techniques for text recognition mainly focus on either visual representation learning or …

Abstract Scene Text Image Super-resolution (STISR) aims to recover high-resolution (HR)
scene text images with visually pleasant and readable text content from the given low …