SOFTWARE FOR RECOGNITION OF CAR NUMBER PLATE
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Abstract
The purpose of this paper is to design and implement an automatic number plate recognition system. The system has still images as the input, and extracts a string corresponding to the plate number, which is used to obtain the output user data from a suitable database. The system extracts data from a license plate and automatically reads it with no prior assumption of background made. License plate extraction is based on plate features, such as texture, and all characters segmented from the plate are passed individually to a character recognition stage for reading. The string output is then used to query a relational database to obtain the desired user data. This particular paper utilizes the intersection of a hat filtered image and a texture mask as the means of locating the number plate within the image. The accuracy of location of the number plate with an image set of 100 images is 68%.
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References
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Lin, Ch.-H., Lin, Y.-S., & Liu, W.-Ch. (2018). An efficient license plate recognition system using convolution neural networks. In 2018 IEEE International Conference on Applied System Invention (pp. 224–227). Japan: IEEE. DOI: https://doi.org/10.1109/ICASI.2018.8394573
Sharma, G. (2018). Performance Analysis of Vehicle Number Plate Recognition System Using Template Matching Techniques. Journal of Information Technology & Software Engineering, 8(2), 1–9. DOI: https://doi.org/10.4172/2165-7866.1000232
Silva, S. & Jung, C. (2018). License plate detection and recognition in unconstrained scenarios. In V. Ferrari, M. Hebert, C. Sminchisescu & Y. Weiss (Eds.), European Conference on Computer Vision (pp. 593–609). Springer, Cham. DOI: https://doi.org/10.1007/978-3-030-01258-8_36
Xie, F., & Zhang, M. (2018). A robust license plate detection and character recognition algorithm based on a combined feature extraction model and BPNN. Journal of Advanced Transportation, 2018, 1–14. DOI: https://doi.org/10.1155/2018/6737314
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