Abstract
Segmentation-based scene text detection algorithms can handle arbitrary shape scene texts and have strong robustness and adaptability, so it has attracted wide attention. Existing segmentation-based scene text detection algorithms usually only segment the pixels in the center region of the text, while ignoring other information of the text region, such as edge information, distance information, etc., thus limiting the detection accuracy of the algorithm for scene text. This paper proposes a plug-and-play module called the Region Multiple Information Perception Module (RMIPM) to enhance the detection performance of segmentation-based algorithms. Specifically, we design an improved module that can perceive various types of information about scene text regions, such as text foreground classification maps, distance maps, direction maps, etc. Experiments on MSRA-TD500 and TotalText datasets show that our method achieves comparable performance with current state-of-the-art algorithms.
Abstract (translated)
基于分割的场景文本检测算法可以处理任意形状的场景文本，具有很强的鲁棒性和适应性，因此吸引了广泛关注。现有的基于分割的场景文本检测算法通常仅在文本的中心区域分割像素，而忽略了文本区域的其他信息，如边缘信息、距离信息等，从而限制了算法对场景文本的检测精度。本文提出了一种可插拔的模块，称为区域多重信息感知模块（RMIPM），以增强基于分割算法的检测性能。具体来说，我们设计了一个改进的模块，可以感知场景文本区域的各种信息，如文本前景分类图、距离图、方向图等。对MSRA-TD500和TotalText数据集的实验结果表明，我们的方法与最先进的算法具有相当的表现。
URL
https://arxiv.org/abs/2401.10017
