• DocumentCode
    1578991
  • Title

    The cooperative approach of genetic algorithm and neural network for the identification of vehicle License Plate number

  • Author

    Thakur, Mukesh ; Raj, Ishank ; Ganesan, P.

  • Author_Institution
    Dept. of Electron. & Control Eng., Sathyabama Univ., Chennai, India
  • fYear
    2015
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    License plate recognition (LPR) plays a major role to detect the plate number of the vehicles, As the number of vehicles increasing day by day so stealing of vehicles, breaking traffic rules, entering restricted area are also increases linearly, so to control these things license plate recognition system is designed. License Plate Recognition (LPR) systems basically consist of 3 main operations are carried out such as: Detection of number plate´s area, Segmentation of plate characters and Recognition of each character. Among this, the detection of plate´s area and recognition of each character is very difficult task due to the different in plate style and various character font. So in order to overcome these problems the genetic algorithm (GA) is used to detect the any type of plate´s area and Artificial neural network (ANN) is used for recognized the any font style of character.
  • Keywords
    genetic algorithms; image segmentation; neural nets; object recognition; traffic engineering computing; ANN; GA; LPR; artificial neural network; genetic algorithm; license plate recognition system; plate characters segmentation; traffic rules; vehicle license plate number identification; Character recognition; Feature extraction; Genetic algorithms; Image segmentation; Licenses; Neural networks; Vehicles; genetic algorithm; license plate recognition; neural network; preprocessing; segmentation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Innovations in Information, Embedded and Communication Systems (ICIIECS), 2015 International Conference on
  • Conference_Location
    Coimbatore
  • Print_ISBN
    978-1-4799-6817-6
  • Type

    conf

  • DOI
    10.1109/ICIIECS.2015.7193090
  • Filename
    7193090