Reading text in real-world scenarios often requires understanding the context surrounding it,
especially when dealing with poor-quality text. However, current scene text recognizers are …

Pre-trained vision-language models~(VLMs) are the de-facto foundation models for various
downstream tasks. However, scene text recognition methods still prefer backbones pre …

Text recognition is a popular research subject with many associated challenges. Despite the
considerable progress made in recent years, the text recognition task itself is still …

The pursuit of high performance on public benchmarks has been the driving force for
research in scene text recognition, and notable progresses have been achieved. However, a …

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 Scene Text Recognition (STR) task needs to consume large-amount data to
develop a powerful recognizer, including visual data like images and linguistic data like …

Linguistic knowledge is of great benefit to scene text recognition. However, how to effectively
model linguistic rules in end-to-end deep networks remains a research challenge. In this …

Attention-based scene text recognizers have gained huge success, which leverages a more
compact intermediate representation to learn 1d-or 2d-attention by a RNN-based encoder …

Over the past few years, several new methods for scene text recognition have been
proposed. Most of these methods propose novel building blocks for neural networks. These …

Abstract Scene Text Recognition (STR), the task of recognizing text against complex image
backgrounds, is an active area of research. Current state-of-the-art (SOTA) methods still …