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Multi-script Text Detection and Classification from Natural Scenes

  • Zaidah IbrahimEmail author
  • Zolidah Kasiran
  • Dino Isa
  • Nurbaity Sabri
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 652)

Abstract

Most of the text detection and script classification approaches from natural scenes only cater for a single script whereas text in natural scenes may come in various scripts. This research proposes a gestalt-based approach for multi-script text detection and classification based on human perception. Human perceptual organization is where humans are able to organize visual input into meaningful information. This approach is based on the figure-ground articulation where we perceive the figure or text as standing in front of the background. Features extracted from wavelet coefficients and MSER is used as input to SVM for text detection and script classification. Experimental results indicate that this approach is competitive with the state of the art text detection and script classification approaches.

Keywords

Gestalt MSER Script classification SVM Text detection Wavelet coefficients 

Notes

Acknowledgments

The authors thank the Ministry of Education and Universiti Teknologi MARA for sponsoring this research under the National Grant No 600-RMI/FRGS 5/3 (165/2013).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2016

Authors and Affiliations

  • Zaidah Ibrahim
    • 1
    Email author
  • Zolidah Kasiran
    • 1
  • Dino Isa
    • 2
  • Nurbaity Sabri
    • 3
  1. 1.Faculty of Computer and Mathematical SciencesUniversiti Teknologi MARAShah AlamMalaysia
  2. 2.Faculty of EngineeringUniversity of Nottingham Malaysia CampusSemenyihMalaysia
  3. 3.Faculty of Computer and Mathematical SciencesUniversiti Teknologi MARA Campus JasinMerlimauMalaysia

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