The vision transformer (ViT) is a state-of-the-art architecture for image recognition tasks that
plays an important role in digital health applications. Medical images account for 90% of the …

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 …

The transformer-based encoder-decoder framework is becoming popular in scene text
recognition, largely because it naturally integrates recognition clues from both visual and …

Text recognition, especially for complex scripts like Chinese, faces unique challenges due to
its intricate character structures and vast vocabulary. Traditional one-hot encoding methods …

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 …

Vision model have gained increasing attention due to their simplicity and efficiency in Scene
Text Recognition (STR) task. However, due to lacking the perception of linguistic knowledge …

The development of artificial intelligence has introduced new perspectives to the field of
hydrological forecasting. However, there is still a lack of research on efficiently identifying …

Text irregularities pose significant challenges to scene text recognizers. Thin-Plate Spline
(TPS)-based rectification is widely regarded as an effective means to deal with them …

Multilingual text recognition (MLTR) systems typically focus on a fixed set of languages,
which makes it difficult to handle newly added languages or adapt to ever-changing data …

Scene text detection has made great progress recently with the wide use of pre-training.
Nonetheless, existing scene text detection methods still suffer from two problems: 1) Limited …