Scene text recognition (STR) has been an active research topic in computer vision for years.
To tackle this challenging problem, numerous innovative methods have been successively …

Scene text spotting is of great importance to the computer vision community due to its wide
variety of applications. Recent methods attempt to introduce linguistic knowledge for …

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 …

Linguistic knowledge has brought great benefits to scene text recognition by providing
semantics to refine character sequences. However, since linguistic knowledge has been …

A novel scene text recognizer based on Vision-Language Transformer (VLT) is presented.
Inspired by Levenshtein Transformer in the area of NLP, the proposed method (named …

The diversity in length constitutes a significant characteristic of text. Due to the long-tail
distribution of text lengths, most existing methods for scene text recognition (STR) only work …

In real applications, new object classes often emerge after the detection model has been
trained on a prepared dataset with fixed classes. Fine-tuning the old model with only new …

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

Due to the flexible representation of arbitrary-shaped scene text and simple pipeline, bottom-
up segmentation-based methods begin to be mainstream in real-time scene text detection …

Scene text recognition (STR) involves the task of reading text in cropped images of natural
scenes. Conventional models in STR employ convolutional neural network (CNN) followed …