Given the ubiquity of handwritten documents in human transactions, Optical Character
Recognition (OCR) of documents have invaluable practical worth. Optical character …

The history of text can be traced back over thousands of years. Rich and precise semantic
information carried by text is important in a wide range of vision-based application …

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

In this paper, we abandon the dominant complex language model and rethink the linguistic
learning process in the scene text recognition. Different from previous methods considering …

Scene text detection and recognition has received increasing research attention. Existing
methods can be roughly categorized into two groups: character-based and segmentation …

In this paper, we present TExt Spotting TRansformers (TESTR), a generic end-to-end text
spotting framework using Transformers for text detection and recognition in the wild. TESTR …

The recent large-scale Contrastive Language-Image Pretraining (CLIP) model has shown
great potential in various downstream tasks via leveraging the pretrained vision and …

Scene text detection and document layout analysis have long been treated as two separate
tasks in different image domains. In this paper, we bring them together and introduce the …

In recent years, end-to-end scene text spotting approaches are evolving to the Transformer-
based framework. While previous studies have shown the crucial importance of the intrinsic …

End-to-end text-spotting, which aims to integrate detection and recognition in a unified
framework, has attracted increasing attention due to its simplicity of the two complimentary …