Neural architecture search (NAS) has attracted increasing attention. In recent years,
individual search methods have been replaced by weight-sharing search methods for higher …

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 …

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 …

Image super-resolution, which is often regarded as a preprocessing procedure of scene text
recognition, aims to recover the realistic features from a low-resolution text image. It has …

Existing text recognition methods usually need large-scale training data. Most of them rely
on synthetic training data due to the lack of annotated real images. However, there is a …

Scene text recognition is a challenging task due to diverse variations of text instances in
natural scene images. Conventional methods based on CNN-RNN-CTC or encoder …

Sample selection approaches are popular in robust learning from noisy labels. However,
how to properly control the selection process so that deep networks can benefit from the …

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

Nowadays, scene text recognition has attracted more and more attention due to its various
applications. Most state-of-the-art methods adopt an encoder-decoder framework with …