Recognition of Car License Plate by Using Dynamical Thresholding and Neural Network with Enhanced Learning Algorithm 


Vol. 9,  No. 1, pp. 119-128, Feb.  2002
10.3745/KIPSTB.2002.9.1.119


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  Abstract

This paper proposes an efficient recognition method of car license plate from the car images by using both the dynamical thresholding and the neural network with enhanced learning algorithm. The car license plate is extracted by the dynamical thresholding based on the structural features and the density rates. Each characters and numbers from the plate is also extracted by the contour tracking algorithm. The enhanced neural network is proposed for recognizing them, which has the algorithm of combining the modified ART1 and the supervised learning method. The proposed method has applied to the real-world car images. The simulation results show that the proposed method has better the extraction rates than the methods with information of the gray brightness and the RGB, respectively. And the proposed method has better recognition performance than the conventional backpropagation neural network.

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[IEEE Style]

K. B. Kim and Y. J. Kim, "Recognition of Car License Plate by Using Dynamical Thresholding and Neural Network with Enhanced Learning Algorithm," The KIPS Transactions:PartB , vol. 9, no. 1, pp. 119-128, 2002. DOI: 10.3745/KIPSTB.2002.9.1.119.

[ACM Style]

Kwang Baek Kim and Young Ju Kim. 2002. Recognition of Car License Plate by Using Dynamical Thresholding and Neural Network with Enhanced Learning Algorithm. The KIPS Transactions:PartB , 9, 1, (2002), 119-128. DOI: 10.3745/KIPSTB.2002.9.1.119.

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