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.

翻译：基于分割的场景文本检测算法能够处理任意形状的场景文本，并具有较强的鲁棒性和适应性，因此受到了广泛关注。现有的基于分割的场景文本检测算法通常仅分割文本中心区域的像素，而忽略了文本区域的其他信息，例如边缘信息、距离信息等，从而限制了算法对场景文本的检测精度。本文提出了一种即插即用模块——区域多信息感知模块（RMIPM），用于增强基于分割算法的检测性能。具体而言，我们设计了一个改进模块，能够感知场景文本区域的多种信息，如文本前景分类图、距离图、方向图等。在MSRA-TD500和TotalText数据集上的实验表明，我们的方法取得了与当前最先进算法相当的性能。