License Plates Detection Using a Gaussian Windows 


Vol. 37,  No. 9, pp. 780-785, Sep.  2012


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  Abstract

In the current study, the authors propose a method for extracting license plate regions by means of a neural network trained to output the plates center of gravity. The method is shown to be effective. Since the learning pattern presentation positions are defined by random numbers, a different pattern is submitted to the neural network for learning each time, which enables it to form a neural network with high universality of coverage. The article discusses issues of the optimal learning surface for a license plate covered by the learning pattern, the effect of suppression learning of the number and headlight sections, as well as the effect of learning pattern enlargement/reduction and of concentration value conversion. Results of evaluation tests based on pictures of 595 vehicles taken at an underground parking garage demonstrated detection rates of 98.5%.

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  Cite this article

[IEEE Style]

Y. Kang and C. Bae, "License Plates Detection Using a Gaussian Windows," The Journal of Korean Institute of Communications and Information Sciences, vol. 37, no. 9, pp. 780-785, 2012. DOI: .

[ACM Style]

Yong-Seok Kang and Cheol-Soo Bae. 2012. License Plates Detection Using a Gaussian Windows. The Journal of Korean Institute of Communications and Information Sciences, 37, 9, (2012), 780-785. DOI: .

[KICS Style]

Yong-Seok Kang and Cheol-Soo Bae, "License Plates Detection Using a Gaussian Windows," The Journal of Korean Institute of Communications and Information Sciences, vol. 37, no. 9, pp. 780-785, 9. 2012.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880