• DocumentCode
    2954425
  • Title

    New License Plate Character Recognition Algorithm Based on ICM

  • Author

    Wu, Jun ; Xiao, ZhiTao

  • Author_Institution
    Sch. of Inf. & Commun. Eng., Tianjin Polytech. Univ., Tianjin, China
  • fYear
    2011
  • fDate
    30-31 July 2011
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    For License Plate Recognition (LPR) system, license plate character recognition rate is seriously affected by image quality. To resolve this problem, a new algorithm is proposed, in which Intersecting Cortical Model (ICM) is applied into license plate character recognition. ICM was derived from several visual cortex models, which can be applied to image feature extraction efficiently. In the new algorithm ICM was utilized to extract three features from size-normalized binary input character image. Based on these features, weighted voting was performed and final estimation of input character was made. Experiment results show that compared with common algorithms based on BP network, the new ICM based algorithm has higher recognition rate and stronger robustness, and is more convenient and flexible.
  • Keywords
    backpropagation; character recognition; feature extraction; neural nets; traffic engineering computing; BP network; LPR system; feature extraction; image quality; intersecting cortical model; license plate character recognition rate; size-normalized binary input character image; visual cortex model; weighted voting; Brain modeling; Character recognition; Feature extraction; Image recognition; Licenses; Neurons; Noise;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control, Automation and Systems Engineering (CASE), 2011 International Conference on
  • Conference_Location
    Singapore
  • Print_ISBN
    978-1-4577-0859-6
  • Type

    conf

  • DOI
    10.1109/ICCASE.2011.5997683
  • Filename
    5997683