MorphText: Deep Morphology Regularized Accurate Arbitrary-Shape Scene Text Detection

Chengpei Xu, Wenjing Jia, Ruomei Wang, Xiaonan Luo, Xiangjian He

Research output: Journal PublicationArticlepeer-review


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.

Original languageEnglish
Pages (from-to)4199-4212
Number of pages14
JournalIEEE Transactions on Multimedia
Publication statusPublished - 5 May 2022


  • Arbitrary-shape scene text detection
  • bottom-up methods
  • deep morphology
  • regularized text segments

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Media Technology
  • Computer Science Applications


Dive into the research topics of 'MorphText: Deep Morphology Regularized Accurate Arbitrary-Shape Scene Text Detection'. Together they form a unique fingerprint.

Cite this