Research and Implementation of a License Plate Recognition Algorithm Based on Hierarchical Classification

Haofei Gao, Wanrong Sun, Xinran Liu, Ming Han

School of Electronic Engineering, Xidian University, Xi’an, China.
School of Life Sciences and Technology, Xidian University, Xi’an, China.

DOI: 10.4236/jcc.2014.22005                

Abstract

This paper proposed an improved method for license plate recognition based on hierarchical classification. First, the method of feature extraction and dimension reduction is presented by finding the optimal wavelet packet basis in the process of wavelet packet decomposition and K-L transform. Then the recognition algorithm is introduced based on feature extraction and hierarchical classification. Finally, the principles and procedures of using support vector machines, Harris corner detection algorithm and digital character classification are explained in detail. Simulation results indicate that the presented recognition algorithm performs well with higher speed and efficiency in recognition.

Keywords

Wavelet Packet; K-L Transform; SVM; Recognition; License Plate

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	V. N. Vapnik, “The Nature of Statistical Learning Theory,” Springer, New York, 1995. http://dx.doi.org/10.1007/978-1-4757-2440-0
[2]	J. T. Chien and C. P. Liao, “Maximum Confidence Hidden Markov Modeling for Face Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30, No. 8, 2008, pp. 606-616. http://dx.doi.org/10.1109/TPAMI.2007.70715
[3]	G. B. Singh and H. Song, “Using Hidden Markov Models in Vehicular Crash Detection,” IEEE Transactions on Vehicular Technology, Vol. 58, No. 3, 2009, pp. 1119-1128. http://dx.doi.org/10.1109/TVT.2008.928904
[4]	W. J. Wei, H. F. Kan and Z. L. Zhu, “The Image Classification Using a Neural Network by Zernike Moment,” IEEE National Aerospace and Electronics Conference, 1992, pp. 254-259.
[5]	S. Sunny and P. S. David, “Feature Extraction Methods Based on Linear Predictive Coding and Wavelet Packet Decomposition for Recognizing Spoken Words in Malayalam,” 2012 International Conference on Advances in Computing and Communications (ICACC), Cochin, Kerala, 2012, pp. 27-30. http://dx.doi.org/10.1109/ICACC.2012.7
[6]	T. Stutz and A. Uhl, “Efficient and Rate-Distortion Optimal Wavelet Packet Basis Selection in JPEG2000,” IEEE Transactions on Multimedia, Vol. 14, No. 2, 2012, pp. 264-277. http://dx.doi.org/10.1109/TMM.2011.2177644
[7]	J. F. Al-Asad, A. Moghadamjoo and Y. Leslie, “Ultrasound Image Denoising through Karhunen-Loeve (K-L) Transform with Overlapping Segments,” IEEE International Symposium on Biomedical Imaging: From Nano to Macro, (ISBI’09), 2009, pp. 318-321.
[8]	D. Xie and J. Q. Zhu, “Research on Method of Main Reducer Assembly Quality Evaluation Based on K-L Transform and Support Vector Machine,” 2011 International Conference on Electric Information and Control Engineering (ICEICE), 2011, pp. 677-680.
[9]	Q. Zhao, J. L. Cao and Y. L. Hu, “Joint Optimization of Feature Selection and Parameters for Multi-class SVM in Skin Symptomatic Recognition,” International Conference on Artificial Intelligence and Computational Intelligence, 2009, pp. 407-411.
[10]	Wang, S. P. Patrick, “Pattern Recognition, Machine Intelligence and Biometrics,” Higher Education Press, Beijing, 2011.
[11]	J. A. K. Suykens, “Least Squares Support Vector Machines,” World Scientific, River Edge, 2002.
[12]	N. Cristianini and J. S. Taylor, “An Introduction to Support Vector Machines and Other Kernel-based Learning Methods,” Publishing House of Electronics Industry, Beijing, 2004.
[13]	C.-C. Chang and C.-J. Lin, “LIBSVM: A Library for Support Vector Machines”. http://www.csie.ntu.edu.tw/~cjlin/libsvm
[14]	C. H. Lin, J. C. Liu and C. H. Ho, “Anomaly Detection Using LibSVM Training Tools,” International Conference on Information Security and Assurance, 2008, pp. 166-171.
[15]	Z. Y. Ye, Y. J. Pei and J. H. Shi, “An Improved Algorithm for Harris Corner Detection,” 2nd International Congress on Image and Signal Processing, 2009, pp. 1-4.
[16]	C. Harris and M. Stephens, “A Combined Corner and Edge Detector,” Proceedings of the 4th Alvey Conference, Manchester, England, 1998, pp. 147-151.