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http://dx.doi.org/10.3807/JOSK.2010.14.4.368

Adaptive Thresholding Technique for Binarization of License Plate Images  

Kim, Min-Ki (Education Research Institute, Department of Computer Science Education, Gyeongsang National University)
Publication Information
Journal of the Optical Society of Korea / v.14, no.4, 2010 , pp. 368-375 More about this Journal
Abstract
Unlike document images, license plate images are mostly captured under uneven lighting conditions. In particular, a shadowed region has sharp intensity variation and sometimes that region has very high intensity by reflected light. This paper presents a new technique for thresholding license plate images. This approach consists of three parts. In the first part, it performs a rough thresholding and classifies the type of license plate to adjust some parameters optimally. Next, it identifies a shadow type and binarizes license plate images by adjusting the window size and location according to the shadow type. And finally, post-processing based on the cluster analysis is performed. Experimental results show that the proposed method outperformed five well-known methods.
Keywords
Adaptive threshold decision; Shadowed license plate image; Cluster analysis;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
Times Cited By Web Of Science : 1  (Related Records In Web of Science)
Times Cited By SCOPUS : 1
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1 M. Sezgin and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” Journal of Electronic Imaging 13, 146-165 (2004).   DOI   ScienceOn
2 Y. Yang and H. Yan, “An adaptive logical method for binarization of degraded document images,” Pattern Recognition 33, 787-807 (2000).   DOI   ScienceOn
3 P. Stathis, E. Kavallieratou, and N. Papamarkos, “An evaluation survey of binarization algorithms on historical documents,” in Proc. of ICPR (Florida, USA, Dec. 2008), pp. 1-4.
4 Y. J. Zhang, “A survey on evaluation methods for image segmentation,” Pattern Recognition 29, 1335-1346 (1996).   DOI   ScienceOn
5 F. Yang, Z. Ma, and M. Xie, “A novel binarization approach for license plate,” in Proc. of Industrial Electronics and Applications (Singapore, May 2006), pp. 1-4.
6 J. Bernsen, “Dynamic thresholding of grey-level images,” in Proc. of ICPR (Paris, France, Oct. 1986), pp. 1251-1255.
7 W. Niblack, An Introduction to Digital Image Processing (Prentice Hall, Englewood Cliffs, NJ, USA, 1986), pp. 115-116.
8 J. Sauvola and M. Pietikainen, “Adaptive document image binarization,” Pattern Recognition 33, 225-236 (2000).   DOI   ScienceOn
9 X.-Y. Yang, K.-L. Kim, and B.-K. Hwang, “An efficient binarization method for vehicle license plate character recognition,” Journal of Korea Multimedia Society 11, 1649-1657 (2008).   과학기술학회마을
10 H.-C. Tan and H. Chen, “A novel car plate verification with adaptive binarization method,” in Proc. of the 7th ICMLC (Kunming, China, July 2008), pp. 12-15.
11 B.-F.Wu, S.-P. Lin, and C.-C. Chiu, “Extracting characters from real vehicle license plates out-of-doors,” IET Computer Vision 1, 2-10 (2007).   DOI   ScienceOn
12 I.-J. Kim, “Multi-window binarization of camera image for document recognition,” in Proc. of IWFHR-9 (Tokyo, Japan, Oct. 2004), pp. 323-327.
13 N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. on SMC 9, 62-66 (1979).
14 Q. Huang, W. Gao, and W. Cai, “Thresholding technique with adaptive window selection for uneven lighting image,” Pattern Recognition Letters 26, 801-808 (2005).   DOI   ScienceOn
15 B. Gatos, I. Pratikakis, and S. J. Perantonis, “Adaptive degraded document image binarization,” Pattern Recognition 39, 317-327 (2006).   DOI   ScienceOn
16 O. D. Trier and A. K. Jain, “Goal-directed evaluation of binarization methods,” IEEE Trans. on PAMI 17, 1191-1201 (1995).   DOI   ScienceOn
17 C. Arth, D. Limberger, and H. Bischof, “Real-time license plate recognition on an embedded DSP-platform,” in Proc. of CVPR (Minneapolis, USA, June 2007), pp. 1-8.
18 P. Comelli, P. Ferragina, M. N. Granieri, and F. Stabile, “Optical recognition of motor vehicle license plates,” IEEE Trans. on Vehicular Technology 44, 790-799 (1995).   DOI   ScienceOn
19 Y.-Q. Yang, J. Bai, R.-L. Tian, and N. Liu, “A vehicle license plate recognition system based on fixed color collocation,” in Proc. of the 4th ICMLC (Guangzhou, China, Aug. 2005), pp. 5394-5397.