Adaptive Local Binarization Method for Recognition of Vehicle License Plates

  • Byeong Rae Lee
  • Kyungsoo Park
  • Hyunchul Kang
  • Haksoo Kim
  • Chungkyue Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3322)


A vehicle license-plate recognition system is commonly composed of three essential parts: detecting license-plate region in the acquired images, extracting individual characters, and recognizing the extracted characters. But in the process, the problems like damage of license-plate and unequal light effect make it difficult to detect accurate vehicle license-plate region and to extract letters in that region. In this paper, to extract characters accurately in the license- plate region, a local adaptive binarization method which is robust under non-uniform lighting environment is proposed. To get better binary images, region- based threshold correction based on a prior knowledge of character arrangement in the license-plate is applied. With the proposed binarization method, 96% of 650 sample vehicle license-plates images are correctly recognized. Compared to existing local threshold selection methods, about 5% of improvement in recognition rate is obtained with the same recognition module based on LVQ.


Character Region License Plate Recognition Module Binarization Method Recognition Failure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Gao, D., Zhou, J.: Car License Plate Detection from Complex Scene. In: Proceedings of International Conference on Signal Processing, pp. 1409–1414 (2000)Google Scholar
  2. 2.
    Rosa, J., Pavlidis, T.: A Shape Analysis Model with Applications to Character Recognition System. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-16, 393–404 (1994)CrossRefGoogle Scholar
  3. 3.
    Khan, N.A., et al.: Synthetic Patttern Recognizer for Vehicle License Plates. IEEE Transaction on Vehicular Technology 4(4), 790–799 (1995)Google Scholar
  4. 4.
    Hrgt, H.A., et al.: A high Performance License Plate Recognition System. In: Proc. IEEE intl. Conf. on System, Man and Cybernetics, vol. 5, pp. 4357–4362 (1998)Google Scholar
  5. 5.
    Gonzales, R.C., Woods, R.E.: Digital Image Procesing, 2nd edn. Prentice Hall, Englewood Cliffs (1992)Google Scholar
  6. 6.
    Hagan, M.T., Demuth, H.B., Beal, M.: Neural Network Design, pp. 14_16-14_23. Chapman & Hall, Boca Raton (1996)Google Scholar
  7. 7.
    Ohya, J., Shio, A., Akamatsu, S.: Recognizing characters in scene images. IEEE Trans.PAMI-16, 214–220 (1994)Google Scholar
  8. 8.
    Otsu, N.: A Threshold Selection Method from Gray-scale histogram. IEEE Trans. on System, Man, and Cyberetics SMC-8, 62–66 (1978)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Byeong Rae Lee
    • 1
  • Kyungsoo Park
    • 2
  • Hyunchul Kang
    • 2
  • Haksoo Kim
    • 3
  • Chungkyue Kim
    • 4
  1. 1.Dept. of Computer ScienceKorea National Open UniversitySeoulKorea
  2. 2.Dept. of Information and Telecommunication EngineeringUniversity of IncheonIncheonKorea
  3. 3.Dept. of Information and Telecommunication EngineeringSungkonghoe UniversitySeoulKorea
  4. 4.Dept. of Computer Science and EngineeringUniversity of IncheonIncheonKorea

Personalised recommendations