Skip to main navigation Skip to search Skip to main content

Text detection in nature scene images using two-stage nontext filtering

  • Qingqing Wang
  • , Yue Lu
  • , Shiliang Sun
  • East China Normal University

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

Abstract

We present a text detection method in natural scene images based on two-stage nontext filtering. Firstly, we detect multi-channel maximally stable extremal regions (MSERs) as character candidates. To reduce the amount of repeating components, we merge the MSERs by choosing the most character-like ones when overlap happens. Then nontext components are filtered out by a two-stage labeling procedure, wherein we combine random forests with CRF. Finally, components labeled as text are grouped into words by an edge-cut strategy, and false positives are eliminated by a HOG-based classifier. The experimental results on the ICDAR2013 database show the effectiveness of the proposed method.

Original languageEnglish
Title of host publication13th IAPR International Conference on Document Analysis and Recognition, ICDAR 2015 - Conference Proceedings
PublisherIEEE Computer Society
Pages106-110
Number of pages5
ISBN (Electronic)9781479918058
DOIs
StatePublished - 20 Nov 2015
Event13th International Conference on Document Analysis and Recognition, ICDAR 2015 - Nancy, France
Duration: 23 Aug 201526 Aug 2015

Publication series

NameProceedings of the International Conference on Document Analysis and Recognition, ICDAR
Volume2015-November
ISSN (Print)1520-5363

Conference

Conference13th International Conference on Document Analysis and Recognition, ICDAR 2015
Country/TerritoryFrance
CityNancy
Period23/08/1526/08/15

Keywords

  • CRF
  • MSERs
  • edge-cut
  • nontext filtering
  • random forests
  • text detection

Fingerprint

Dive into the research topics of 'Text detection in nature scene images using two-stage nontext filtering'. Together they form a unique fingerprint.

Cite this