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 …

Text in natural scene images plays a vital role in scene understanding. It contains a rich and
abundant amount of valuable semantic information useful in many applications such as …

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

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 …

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 …

Unconstrained handwritten text recognition is a challenging computer vision task. It is
traditionally handled by a two-step approach, combining line segmentation followed by text …

Linguistic knowledge has brought great benefits to scene text recognition by providing
semantics to refine character sequences. However, since linguistic knowledge has been …

A novel scene text recognizer based on Vision-Language Transformer (VLT) is presented.
Inspired by Levenshtein Transformer in the area of NLP, the proposed method (named …

The diversity in length constitutes a significant characteristic of text. Due to the long-tail
distribution of text lengths, most existing methods for scene text recognition (STR) only work …

Typical text recognition methods rely on an encoder-decoder structure, in which the encoder
extracts features from an image, and the decoder produces recognized text from these …