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License Plate Detection Using Neural Networks

  • Luis Carrera
  • Marco Mora
  • José Gonzalez
  • Francisco Aravena
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5518)

Abstract

This work presents a new method for license plate detection using neural networks in gray scale images. The method proposes a multiple classification strategy based on a Multilayer Perceptron. It consists of many classifications of one image using several shifted window grids. If a pixel belongs or not to the licence plate is determined by the most frequent answer given by the different classifications. The result becomes more precise by means of morphological operations and heuristic rules related to shape and size of the license plate zone. The whole method detects the license plates precisely with a low error rate under non-controlled environments.

Keywords

Intelligence Transportation System Heuristic Rule License Plate Pulse Couple Neural Network Frequent Answer 
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.

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References

  1. 1.
    Anagnostopoulos, C., Anagnostopoulos, I., Loumos, V., Kayafas, E.: A License Plate-Recognition Algorithm for Intelligent Transportation System Applications. IEEE Transaction on Intelligence Transportation Systems 7, 377–392 (2006)CrossRefGoogle Scholar
  2. 2.
    Al-Hmouz, R., Challa, S.: License Plate Localization based on a Probabilistic Model. Machine Vision and Applications (2008)Google Scholar
  3. 3.
    Chang, S., Chen, L., Chung, Y., Wan, S.: Automatic License Plate Recognition. IEEE Transaction on Intelligence Transportation Systems 5, 42–53 (2004)CrossRefGoogle Scholar
  4. 4.
    Zhang, C., Sun, G., Chen, D., Zhao, T.: A Rapid Locating Method of Vehicle License Plate based on Characteristics of Characters, Connection and Projection. In: 2nd IEEE Conference on Industrial Electronics and Applications, pp. 2546–2549 (2007)Google Scholar
  5. 5.
    Kwasnicka, H., Wawrzyniak, B.: License Plate Localization and Recognition in Camera Pictures. In: 3rd Symposium on Methods of Artificial Intelligence (AI-METH 2002), pp. 243–246 (2002)Google Scholar
  6. 6.
    Kamat, V., Ganesan, S.: An Efficient Implementation of the Hough Transform for Detecting Vehicle License Plates using DSP’S. In: Real-Time Technology and Applications Symposium, pp. 58–59 (2006)Google Scholar
  7. 7.
    Kim, K., Kim, K., Kim, J.: Learning-based Approach for License Plate Recognition. In: IEEE Signal Processing Society Workshop, vol. 2, pp. 614–623 (2000)Google Scholar
  8. 8.
    Porikli, F., Kocak, T.: Robust License Plate Detection using Covariance Descriptor in a Neural Network Framework. In: IEEE International Conference on Video and Signal Based Surveillance (AVSS 2006), p. 107 (2006)Google Scholar
  9. 9.
    Li, Y., Li, M., Lu, Y., Yang, M., Zhou, C.: A new Text Detection Approach Based on BP Neural Network for Vehicle License Plate Detection in Complex Background. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds.) ISNN 2007. LNCS, vol. 4492, pp. 842–850. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Kim, K., Jung, K., Kim, J.: Color Texture-based Object Detection: An Application to License Plate Localization. In: Lee, S.-W., Verri, A. (eds.) SVM 2002. LNCS, vol. 2388, pp. 321–335. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  11. 11.
    Yuan, X., Wang, L., Zhu, M.: Car Plate Localization using Modified PCNN in Complicated Environment. In: Huang, D.-S., Li, K., Irwin, G.W. (eds.) ICIC 2006. LNCS, vol. 4114, pp. 1116–1124. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    Gonzalez, R., Woods, R., Eddins, S.: Digital Image Processing, 2nd edn. Prentice-Hall, Englewood Cliffs (2002)Google Scholar
  13. 13.
    Funahashi, K.: On the Aproximate Realization of Continuous Mappings by Neural Network. Neural Networks 2, 183–192 (1989)CrossRefGoogle Scholar
  14. 14.
    Foresee, D., Hagan, M.: Gauss-Newton Approximation to Bayesian Learning. In: Proceedings of the International Joint Conference on Neural Networks, pp. 1930–1935 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Luis Carrera
    • 1
  • Marco Mora
    • 1
  • José Gonzalez
    • 2
  • Francisco Aravena
    • 2
  1. 1.Les Fous du Pixel Image Processing Research Group Department of Computer ScienceCatholic University of MauleTalcaChile
  2. 2.TUTELKANTalcaChile

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