Abstract
The task of vehicle identification can be solved by vehicle license plate recognition. It can be used in many applications such as entrance admission, security, parking control, airport or harbor cargo control, road traffic control, speed control and so on. Different Neural Network for character identification like Probabilistic Neural Network and Feed-Forward Back-propagation Neural Network has been used and compared. This paper proposes the use of Sobel operator to identify the edges in the image and to extract the License plate. After extraction of license plate the characters are isolated and passed to character identification system. The method used to identify characters are Probabilistic Neural Network with 108 neurons which gives accuracy of 91.32%, Probabilistic Neural Network with 35 neurons which gives accuracy of 96.73% and Feed Forward Back Propagation Neural Network which gives accuracy of 96.73%.
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Koche, K., Patil, V., Chaudhari, K. (2011). Study of Probabilistic Neural Network and Feed Forward Back Propogation Neural Network for Identification of Characters in License Plate. In: Das, V.V., Thankachan, N., Debnath, N.C. (eds) Advances in Power Electronics and Instrumentation Engineering. PEIE 2011. Communications in Computer and Information Science, vol 148. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20499-9_2
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DOI: https://doi.org/10.1007/978-3-642-20499-9_2
Publisher Name: Springer, Berlin, Heidelberg
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