• DocumentCode
    2148136
  • Title

    An Efficient Method for Correcting Vehicle License Plate Tilt

  • Author

    Deb, Kaushik ; Vavilin, Andrey ; Jo, Kang-Hyun

  • Author_Institution
    Dept. of Electr. Eng. & Inf. Syst., Univ. of Ulsan, Ulsan, South Korea
  • fYear
    2010
  • fDate
    14-16 Aug. 2010
  • Firstpage
    127
  • Lastpage
    132
  • Abstract
    Tilt correction is a very crucial and inevitable task in the automatic recognition of the vehicle license plate (VLP). In this paper, according to the least square fitting with perpendicular offsets (LSFPO) the VLP region is fitted to a straight line. After the line slope is obtained, rotation angle of the VLP is estimated. Then the whole image is rotated for tilt correction in horizontal direction by this angle. Tilt correction in vertical direction by inverse affine transformation is proposed for removing shear from the LP candidates. Despite the success of VLP detection approaches in the past decades, a few of them can effectively locate license plate (LP), even when vehicle bodies and LPs have similar color. A common drawback of color-based VLP detection is the failure to detect the boundaries or border of LPs. In this paper, we propose a modified recursive labeling algorithm for solving this problem and detecting candidate regions. According to different colored LP, these candidate regions may include LP regions. Geometrical properties of the LP such as area, bounding box and aspect ratio are then used for classification. Various LP images were used with a variety of conditions to test the proposed method and results are presented to prove its effectiveness.
  • Keywords
    affine transforms; computational geometry; curve fitting; image colour analysis; image recognition; least squares approximations; traffic engineering computing; automatic vehicle license plate recognition; inverse affine transformation; least square fitting; line slope estimation; recursive labeling algorithm; rotation angle estimation; vehicle license plate tilt correction method; Histograms; Image color analysis; Image segmentation; Labeling; Licenses; Object segmentation; Vehicles; affine transformation; and recursive labeling algorithm; least square fitting with perpendicular offsets (LSFPO); tilt correction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Granular Computing (GrC), 2010 IEEE International Conference on
  • Conference_Location
    San Jose, CA
  • Print_ISBN
    978-1-4244-7964-1
  • Type

    conf

  • DOI
    10.1109/GrC.2010.135
  • Filename
    5576175