Abstract
Vehicle number plate detection and recognition (VNPR) is a pioneering methodology which has a large impact on the development of road safety, automation in toll collection, transportation efficacy, and support to the traffic authorities. In this paper, a number plate detection and recognition system for toll collection is presented. Use of large sample data sets has made the system efficient and robust enough. Contrast enhancement is a preprocessing used followed by conventional techniques to locate the number plate. The percentage accuracy in locating the number plate in a given image is 94.87%. Horizontal and vertical profiles with a ratio of 1:2 are used to separate characters in the detected number plate. Backpropagation neural network is applied on the extracted characters to recognize them for authentication of license plate. The presented system is compared with other conventional methods for evaluating its effectiveness and efficiency. The average number plate recognition accuracy of the proposed system is 90.21%.
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Chavan, S.S., Varma, S.L. (2021). Vehicle Number Plate Recognition for Toll System. In: Deshpande, P., Abraham, A., Iyer, B., Ma, K. (eds) Next Generation Information Processing System. Advances in Intelligent Systems and Computing, vol 1162 . Springer, Singapore. https://doi.org/10.1007/978-981-15-4851-2_19
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DOI: https://doi.org/10.1007/978-981-15-4851-2_19
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