Bottom-up text detection methods play an important role in arbitrary-shape scene text detection but there are two restrictions preventing them from achieving their great potential, i.e., 1) the accumulation of false text segment detections, which affects subsequent processing, and 2) the difficulty of building reliable connections between text segments. Targeting these two problems, we propose a novel approach, named ``MorphText", to capture the regularity of texts by embedding deep morphology for arbitrary-shape text detection. Towards this end, two deep morphological modules are designed to regularize text segments and determine the linkage between them. First, a Deep Morphological Opening (DMOP) module is constructed to remove false text segment detections generated in the feature extraction process. Then, a Deep Morphological Closing (DMCL) module is proposed to allow text instances of various shapes to stretch their morphology along their most significant orientation while deriving their connections. Extensive experiments conducted on four challenging benchmark datasets (CTW1500, Total-Text, MSRA-TD500 and ICDAR2017) demonstrate that our proposed MorphText outperforms both top-down and bottom-up state-of-the-art arbitrary-shape scene text detection approaches.

翻译：自底向上的文本检测方法在任意形状场景文本检测中发挥着重要作用，但存在两个限制阻碍其发挥巨大潜力：1）虚假文本片段检测的累积，影响后续处理；2）难以在文本片段之间建立可靠连接。针对这两个问题，我们提出名为“MorphText”的新方法，通过嵌入深度形态学来捕捉文本的规律性，从而实现任意形状文本检测。为此，设计了两个深度形态学模块：首先构建深度形态学开运算模块，用于消除特征提取过程中产生的虚假文本片段检测；其次提出深度形态学闭运算模块，使各种形状的文本实例能够沿其最显著方向延伸形态并建立连接。在四个具有挑战性的基准数据集（CTW1500、Total-Text、MSRA-TD500和ICDAR2017）上进行的大量实验表明，所提出的MorphText方法在性能和鲁棒性上均优于现有最先进的任意形状场景文本检测方法（包括自顶向下和自底向上两类方法）。