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 …

We present TextMonkey, a large multimodal model (LMM) tailored for text-centric tasks. Our
approach introduces enhancement across several dimensions: By adopting Shifted Window …

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

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 …

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

This paper aims to re-assess scene text recognition (STR) from a data-oriented perspective.
We begin by revisiting the six commonly used benchmarks in STR and observe a trend of …

A crucial component for the scene text based reasoning required for TextVQA and TextCaps
datasets involve detecting and recognizing text present in the images using an optical …