One-to-one set matching is a key design for DETR to establish its end-to-end capability, so
that object detection does not require a hand-crafted NMS (non-maximum suppression) to …

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 …

Recently, transformer-based methods have achieved promising progresses in object
detection, as they can eliminate the post-processes like NMS and enrich the deep …

Degraded document binarization has received keen attention due to its vital influence on
subsequent document analysis tasks. In this study, we propose a novel Degraded Document …

We exploit the potential of the large-scale Contrastive Language-Image Pretraining (CLIP)
model to enhance scene text detection and spotting tasks, transforming it into a robust …

As a prerequisite step of scene text reading, scene text detection is known as a challenging
task due to natural scene text diversity and variability. Most existing methods either adopt …

Computational perception has indeed been dramatically modified and reformed from
handcrafted feature-based techniques to the advent of deep learning. Scene text …

Generally, pre-training and long-time training computation are necessary for obtaining a
good-performance text detector based on deep networks. In this paper, we present a new …

Recent text detection frameworks require several handcrafted components such as anchor
generation, non-maximum suppression (NMS), or multiple processing stages (eg label …

Due to the increasing applications of surveillance and monitoring such as person re-
identification, vehicle re-identification and sports events tracking, the necessity of text …