Memetic Computing

, Volume 8, Issue 3, pp 235–251 | Cite as

A memetic-based fuzzy support vector machine model and its application to license plate recognition

  • Hussein Samma
  • Chee Peng Lim
  • Junita Mohamad Saleh
  • Shahrel Azmin Suandi
Regular Research Paper


In this paper, a novel fuzzy support vector machine (FSVM) coupled with a memetic particle swarm optimization (MPSO) algorithm is introduced. Its application to a license plate recognition problem is studied comprehensively. The proposed recognition model comprises linear FSVM classifiers which are used to locate a two-character window of the license plate. A new MPSO algorithm which consists of three layers i.e. a global optimization layer, a component optimization layer, and a local optimization layer is constructed. During the construction process, MPSO performs FSVM parameters tuning, feature selection, and training instance selection simultaneously. A total of 220 real Malaysian car plate images are used for evaluation. The experimental results indicate the effectiveness of the proposed model for undertaking license plate recognition problems.


Fuzzy support vector machine Memetic particle swarm optimization Licence plate recognition 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Hussein Samma
    • 1
    • 2
  • Chee Peng Lim
    • 3
  • Junita Mohamad Saleh
    • 1
  • Shahrel Azmin Suandi
    • 1
  1. 1.Imaging and Computational Intelligence Group (ICI), School of Electrical and Electronic EngineeringUniversity of Science MalaysiaPenangMalaysia
  2. 2.Faculty of EducationUniversity of AdenShabowahYemen
  3. 3.Institute for Intelligent Systems Research and InnovationDeakin UniversityVictoriaAustralia

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