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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
Al-Hmouz, R., Challa, S.: License Plate Localization based on a Probabilistic Model. Machine Vision and Applications (2008)
Chang, S., Chen, L., Chung, Y., Wan, S.: Automatic License Plate Recognition. IEEE Transaction on Intelligence Transportation Systems 5, 42–53 (2004)
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)
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)
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)
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)
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)
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)
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)
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)
Gonzalez, R., Woods, R., Eddins, S.: Digital Image Processing, 2nd edn. Prentice-Hall, Englewood Cliffs (2002)
Funahashi, K.: On the Aproximate Realization of Continuous Mappings by Neural Network. Neural Networks 2, 183–192 (1989)
Foresee, D., Hagan, M.: Gauss-Newton Approximation to Bayesian Learning. In: Proceedings of the International Joint Conference on Neural Networks, pp. 1930–1935 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Carrera, L., Mora, M., Gonzalez, J., Aravena, F. (2009). License Plate Detection Using Neural Networks. In: Omatu, S., et al. Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living. IWANN 2009. Lecture Notes in Computer Science, vol 5518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02481-8_186
Download citation
DOI: https://doi.org/10.1007/978-3-642-02481-8_186
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02480-1
Online ISBN: 978-3-642-02481-8
eBook Packages: Computer ScienceComputer Science (R0)