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 …

Recognizing text in natural images is a challenging task with many unsolved problems.
Different from those in documents, words in natural images often possess irregular shapes …

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 …

We address the problem of image feature learning for scene text recognition. The image
features in the state-of-the-art methods are learned from large-scale synthetic image …

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 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 …

The exploration of linguistic information promotes the development of scene text recognition
task. Benefiting from the significance in parallel reasoning and global relationship capture …

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

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 …

Abstract Existing Scene Text Recognition (STR) methods typically use a language model to
optimize the joint probability of the 1D character sequence predicted by a visual recognition …