Scene text detection using sequential nontext filtering

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

We present a scene text detection method based on sequential nontext filtering. Firstly, we start our work with multi-channel maximally stable extremal region (MSER) detection. Then nontext components are eliminated by a four-stage sequential nontext filtering strategy which consists of inner-channel MSER pruning, between-channel MSER pruning, unary feature-based nontext filtering, and binary feature-based nontext filtering. Finally, text components are grouped into words and false positives are eliminated. The proposed method achieves the state-of-the-art on the ICDAR2013 database when compared with some existing methods.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
PublisherIEEE Computer Society
Pages1742-1746
Number of pages5
ISBN (Electronic)9781479983391
DOIs
StatePublished - 9 Dec 2015
EventIEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada
Duration: 27 Sep 201530 Sep 2015

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2015-December
ISSN (Print)1522-4880

Conference

ConferenceIEEE International Conference on Image Processing, ICIP 2015
Country/TerritoryCanada
CityQuebec City
Period27/09/1530/09/15

Keywords

  • MSER
  • binary features
  • repeating components
  • sequential nontext filtering
  • unary features

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