Scene text recognition (STR) enables computers to read text in natural scenes such as
object labels, road signs and instructions. STR helps machines perform informed decisions …

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 …

Many new proposals for scene text recognition (STR) models have been introduced in
recent years. While each claim to have pushed the boundary of the technology, a holistic …

In this article, we present Convoluitional Attention Networks (CAN) for unconstrained scene
text recognition. Recent dominant approaches for scene text recognition are mainly based …

Nowadays, scene text recognition has attracted more and more attention due to its various
applications. Most state-of-the-art methods adopt an encoder-decoder framework with …

Dominant scene text recognition models commonly contain two building blocks, a visual
model for feature extraction and a sequence model for text transcription. This hybrid …

In this paper, we explore the potential of the Contrastive Language-Image Pretraining (CLIP)
model in scene text recognition (STR), and establish a novel Symmetrical Linguistic Feature …

Recent dominant approaches for scene text recognition are mainly based on convolutional
neural network (CNN) and recurrent neural network (RNN), where the CNN processes …

Driven by deep learning and a large volume of data, scene text recognition has evolved
rapidly in recent years. Formerly, RNN-attention-based methods have dominated this field …

This paper aims to re-assess scene text recognition (STR) from a data-oriented perspective.
We begin by revisiting the six commonly used benchmarks in STR and observe a trend of …