DocumentCode :
681442
Title :
License plate localization based on edge-geometrical features using morphological approach
Author :
Jinn-Li Tan ; Abu-Bakar, Syed A. R. ; Mokji, M.M.
Author_Institution :
ECE Dept., Univ. Teknol. Malaysia, Skudai, Malaysia
fYear :
2013
fDate :
15-18 Sept. 2013
Firstpage :
4549
Lastpage :
4553
Abstract :
Malaysian car plates in general appear in different character styles, types (either single or double row), sizes, spacing and character counts. Such variations cause even detecting and localizing these plates a difficult problem. The problem of localization is aggravated further during night time due to poor illumination. In this paper, we introduce the idea of edge-geometrical features in detecting these plates. The edge part is obtained from the use of Difference of Gaussian operation followed by Sobel vertical edge mask. Prior to that, gamma correction is applied to increase the detection of edges. We then apply morphological operations to get the plate region candidates. Using these regions, together with the edge image, we calculate geometrical features of these regions and use rule-based classifier to correctly identify the true plate region. Finally, we test out method using our own data set which contained 250 images captured during day time and 100 images captured during night time. The result of the proposed method shows 96.9% success rate.
Keywords :
edge detection; feature extraction; image classification; object detection; Malaysian car plates; Sobel vertical edge mask; difference of Gaussian operation; edge detection; edge-geometrical features; gamma correction; license plate localization; morphological approach; plate detection; plate region candidates; rule-based classifier; Difference of Gaussian; Gamma correction; Rule-based classifier; Sobel vertical mask;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2013 20th IEEE International Conference on
Conference_Location :
Melbourne, VIC
Type :
conf
DOI :
10.1109/ICIP.2013.6738937
Filename :
6738937
Link To Document :
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