Text recognition is a long-standing research problem for document digitalization. Existing
approaches are usually built based on CNN for image understanding and RNN for char …

Context-aware STR methods typically use internal autoregressive (AR) language models
(LM). Inherent limitations of AR models motivated two-stage methods which employ an …

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 …

Scene text recognition is a crucial area of research in computer vision. However, current
mainstream scene text recognition models suffer from incomplete feature extraction due to …

The growing number of images with text taken from a natural position increases the amount
of text distortion. Some challenges come because of distortion, curvature, or blur which …

Scene text image super-resolution (STISR) aims to enhance the resolution and visual quality
of low-resolution scene text images, thereby improving the performance of some text-related …

Attention-based encoder–decoder scene text recognition (STR) architectures have been
proven effective in recognizing text in the real world, thanks to their ability to learn an internal …

The integration of an attention mechanism has played a crucial role in many recent scene
text recognition (STR) methods. It enables the capture of spatial feature dependencies …

Human readers can accurately count how many letters are in a word (eg, 7 in``buffalo''),
remove a letter from a given position (eg,``bufflo'') or add a new one. The human brain of …

An autoregressive (AR) decoder for scene text recognition (STR) requires numerous
generation steps to decode a text image character by character but can yield high …