  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.
