Segmentation-based scene text detection algorithms that are accurate to the pixel level can
satisfy the detection of arbitrary shape scene text and have received widespread attention …

Recently, regression-based methods, which predict parameterized text shapes for text
localization, have gained popularity in scene text detection. However, the existing …

Scene text spotting, a unified framework between text detection and text recognition, has
made great progress in recent years. Existing methods usually adopt the fully-supervised …

Weakly-supervised learning has emerged as a compelling method for object detection by
reducing the fully annotated labels requirement in the training procedure. Recently, some …

Existing segmentation-based text detection methods generally face the problems of
insufficient receptive fields, insufficient text information filtering, and difficulty in balancing …

With the widespread use of containers, the demand for Container Marking Detection and
Recognition (CMDR) is gradually increasing. The use of deep learning algorithms can …

The detecting arbitrary shape text is a challenging task due to the significant variation in text
shape, size, and aspect ratio, as well as the complexity of scene backgrounds. The …

Despite significant advances in continual semantic segmentation (CSS), they still rely on the
pixel-level annotation to train models, which is time-consuming and labor-intensive …

Recently, scene text detection has received significant attention due to its wide applications.
Accurate detection in complex scenes of multiple scales, orientations, and curvature remains …

Natural scene text detection has made significant progress in the era of deep learning.
However, existing methods still exhibit deficiencies when faced with challenges such as …