Abstract Models based on extremely deep convolutional networks and attention
mechanisms now have powerful feature extraction abilities, improving text recognition …

Super-resolution of scene text images represents a formidable computational problem,
marred by a myriad of intricate challenges. This paper focuses on the specific hurdles that …

Visual understanding, such as image caption generation, has received extensive attention.
Describing images with textual information is one way to help people achieve barrier-free …

Traffic text detection is an important and meaningful research task as it can provide
abundant semantic information for autonomous driving. Although major breakthroughs have …

Extracting text from natural scene images has become a vital issue. The uncertainty of size,
color, background, and alignment of the characters make text recognition in natural scene …

Contextual Text Block Detection (CTBD) is the task of identifying coherent text blocks within
the complexity of natural scenes. Previous methodologies have treated CTBD as either a …

Text Recognition (TR) refers to the research area that focuses on retrieving textual
information from images, a topic that has seen significant advancements in the last decade …

Handwritten character recognition (HCR) has become a growing research, owing to its
several applications in processing the images, recognizing the patterns, communication …

As an important technique for visual perception, text recognition has been an active and
long-standing research topic in the field of computer vision. With the rapid development of …

In the realm of image reconstruction, deep learning-based super-resolution (SR) has
established itself as a prevalent technique, particularly in the domain of text image …