Journal of the Optical Society of Korea

  • 제14권4호
  • /
  • Pages.368-375
  • /
  • 2010
  • /
  • 1226-4776(pISSN)
  • /
  • 2093-6885(eISSN)
한국광학회 (Optical Society of Korea)
권호 표지
DOI QR코드

DOI QR Code

Adaptive Thresholding Technique for Binarization of License Plate Images

  • Kim, Min-Ki (Education Research Institute, Department of Computer Science Education, Gyeongsang National University)
  • 투고 : 2010.10.15
  • 심사 : 2010.12.08
  • 발행 : 2010.12.25
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

참고문헌

  1. 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.
  2. 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). https://doi.org/10.1109/25.467963
  3. N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. on SMC 9, 62-66 (1979).
  4. 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.
  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). https://doi.org/10.1016/S0031-3203(99)00055-2
  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).
  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). https://doi.org/10.1049/iet-cvi:20050132
  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. Q. Huang, W. Gao, and W. Cai, “Thresholding technique with adaptive window selection for uneven lighting image,” Pattern Recognition Letters 26, 801-808 (2005). https://doi.org/10.1016/j.patrec.2004.09.035
  14. B. Gatos, I. Pratikakis, and S. J. Perantonis, “Adaptive degraded document image binarization,” Pattern Recognition 39, 317-327 (2006). https://doi.org/10.1016/j.patcog.2005.09.010
  15. O. D. Trier and A. K. Jain, “Goal-directed evaluation of binarization methods,” IEEE Trans. on PAMI 17, 1191-1201 (1995). https://doi.org/10.1109/34.476511
  16. M. Sezgin and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” Journal of Electronic Imaging 13, 146-165 (2004). https://doi.org/10.1117/1.1631315
  17. Y. Yang and H. Yan, “An adaptive logical method for binarization of degraded document images,” Pattern Recognition 33, 787-807 (2000). https://doi.org/10.1016/S0031-3203(99)00094-1
  18. 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.
  19. Y. J. Zhang, “A survey on evaluation methods for image segmentation,” Pattern Recognition 29, 1335-1346 (1996). https://doi.org/10.1016/0031-3203(95)00169-7

피인용 문헌

  1. Region-based Corner Detection by Radial Projection vol.15, pp.2, 2011, https://doi.org/10.3807/JOSK.2011.15.2.152
  2. Application independent localisation of vehicle plate number using multi-window-size binarisation and semi-hybrid genetic algorithm vol.2018, pp.2, 2018, https://doi.org/10.1049/joe.2017.0815