License plate localization using a statistical analysis of Discrete Fourier Transform signal

Abstract License Plate Recognition (LPR) is a well-known problem and it has developed as a coherent framework. Research continues on the topic due to the diversity of license plates and outdoor illumination conditions which require attention. One of the most important steps in LPR is the localization part where license plates are extracted from video captured images. In this article we introduce a new approach of plate localization using a statistical analysis of Discrete Fourier Transform of the plate signal. The plate signal is represented by five statistics: strength of the signal, normalized maximum amplitude, frequency of maximum amplitude, frequency center and frequency spread. Combining with the color-based histogram thresholding, the method achieves 97.27% accuracy using plate signals from binary images. Comparative analysis is also reported.

[1]  Khalid Aboura.  Automatic thresholding of license plate , 2008, Int. J. Autom. Control..

[2]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[3]  Zhang Hao-peng,et al.  License Plates Automatic Location Based on Intensity Variance and Edge Density , 2011 .

[4]  Beatriz Diaz Acosta,et al.  Experiments in Image Segmentation for Automatic US License Plate Recognition , 2004 .

[5]  Fa-Liang Chang,et al.  Automatic License-Plate Location and Recognition Based on Feature Salience , 2009, IEEE Transactions on Vehicular Technology.

[6]  Abdul Rahman Ramli,et al.  Vertical-Edge-Based Car-License-Plate Detection Method , 2013, IEEE Transactions on Vehicular Technology.

[7]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[8]  Dave Elliman,et al.  A review of segmentation and contextual analysis techniques for text recognition , 1990, Pattern Recognit..

[9]  Subhash Challa,et al.  License plate localization based on a probabilistic model , 2008, Machine Vision and Applications.

[10]  Jing-Ming Guo,et al.  License Plate Localization and Character Segmentation with feedback self-learning and hybrid-binarization techniques , 2008, TENCON 2007 - 2007 IEEE Region 10 Conference.

[11]  E.D. Di Claudio,et al.  Car plate recognition by neural networks and image processing , 1998, ISCAS '98. Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (Cat. No.98CH36187).

[12]  Desmond C. McLernon,et al.  A vehicle license plate detection method using region and edge based methods , 2013, Comput. Electr. Eng..

[13]  Massimo Piccardi,et al.  Multi-Frame Moving Object Track Matching Based on an Incremental Major Color Spectrum Histogram Matching Algorithm , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[14]  Sei-Wang Chen,et al.  Automatic license plate recognition , 2004, IEEE Transactions on Intelligent Transportation Systems.

[15]  Ioannis Anagnostopoulos,et al.  A License Plate-Recognition Algorithm for Intelligent Transportation System Applications , 2006, IEEE Transactions on Intelligent Transportation Systems.

[16]  Qi Tian,et al.  Principal Visual Word Discovery for Automatic License Plate Detection , 2012, IEEE Transactions on Image Processing.

[17]  Pengfei Shi,et al.  An Algorithm for License Plate Recognition Applied to Intelligent Transportation System , 2011, IEEE Transactions on Intelligent Transportation Systems.

[18]  Kwang In Kim,et al.  Learning-based approach for license plate recognition , 2000, Neural Networks for Signal Processing X. Proceedings of the 2000 IEEE Signal Processing Society Workshop (Cat. No.00TH8501).