Robust Text Detection in Natural Scene Images

University of Science and Technology Beijing · Xi’an Jiaotong-Liverpool University · +2 more institutions

PubMed
Indexed inarxivcrossrefpubmed

Abstract

Text detection in natural scene images is an important prerequisite for many content-based image analysis tasks. In this paper, we propose an accurate and robust method for detecting texts in natural scene images. A fast and effective pruning algorithm is designed to extract Maximally Stable Extremal Regions (MSERs) as character candidates using the strategy of minimizing regularized variations. Character candidates are grouped into text candidates by the single-link clustering algorithm, where distance weights and clustering threshold are learned automatically by a novel self-training distance metric learning algorithm. The posterior probabilities of text candidates corresponding to non-text are estimated…

Citation impact

659
total citations
FWCI
45.56
Percentile
100%
References
55
Citations per year

Authors

4

Topics & keywords

Keywords
  • Computer science
  • Artificial intelligence
  • Cluster analysis
  • Text detection
  • Classifier (UML)
  • Pattern recognition (psychology)
  • Character (mathematics)
  • Metric (unit)
UN Sustainable Development Goals
  • Quality Education
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