Abstract
In the last twenty years, the number of vehicles has increased rapidly in our country. With this increase, it is gradually more difficult to track each vehicle for effective law enforcement and traffic management purposes. The growing rate of cars brings upon itself the need to rapidly check the lawfulness of the drivers. When traveling, there are several legal obligations a driver has to fulfill according to the law. In this paper, we proposed an automatic license plate recognition (ALPR) system is a digital image processing procedure that identifies the vehicle number plate without human involvement. It is a computer system that automatically detects and recognizes any digital image on the number plate. This system involves image preprocessing and edge detection using a canny edge detection algorithm, regional localization, and pytesseract used to identify the English characters in the number plate efficiently as well as with less computational complexity. This system simulates an accuracy rate of 97% (approximate) with a comparison of the average processing time of 0.75 s. Our automated system might be highly effective in real-time traffic control, security enhancement in parking lots in shopping malls, hospitals, airports, as well as in electronic toll collection.
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References
Kulkarni P, Khatri A, Banga P, Shah K (2009) Automatic number plate recognition (ANPR) system for Indian conditions. In: 2009 19th International conference Radioelektronika, Bratislava, pp 111–114. https://doi.org/10.1109/RADIOELEK.2009.5158763
Prabhakar P, Anupama P (2014) A novel design for vehicle license plate detection and recognition. In: IEEE 2nd International conference on current trends in engineering and technology (ICCTET), pp 7–12
Kumar A, Verma D (2020) Number plate reorganization using image processing and machine learning approaches: a review. In: 2020 International conference on computer communication and informatics (ICCCI), Coimbatore, India, pp 1–4. https://doi.org/10.1109/ICCCI48352.2020.9104199]
Panahi R, Gholampour I (2017) Accurate detection and recognition of dirty vehicle plate numbers for high-speed applications. IEEE Trans Intell Transp Syst 18(4):767–779. https://doi.org/10.1109/TITS.2016.2586520
Anagnostopoulos CNE, Anagnostopoulos IE, Loumos V, Kayafas E (2006) A license plate-recognition algorithm for intelligent transportation system applications. IEEE Trans Intell Transp Syst 7(3):377–392
Kumari S, Gupta L, Gupta P (2017) Automatic license plate recognition using OpenCV and neural network. Int J Comput Sci Trends Technol (IJCST) 5(3):114–118
Nguwi YY, Lim WJ (2015) Number plate recognition in noisy image. In: 2015 8th International Congress on image and signal processing (CISP), Shenyang, pp 476–480. https://doi.org/10.1109/CISP.2015.7407927
Pechiammal B, Renjith JA (2017) An efficient approach for automatic license plate recognition system. In: 2017 Third international conference on science technology engineering & management (ICONSTEM), Chennai, pp 121–129. https://doi.org/10.1109/ICONSTEM.2017.8261267
Menon A, Omman B (2018) Detection and recognition of multiple license plate from still images. In: 2018 International conference on circuits and systems in digital enterprise technology (ICCSDET), Kottayam, India, pp 1–5. https://doi.org/10.1109/ICCSDET.2018.8821138
Sivagopal M, Soundarya R, Lakshmanan S (2019) Vehicle insurance verification system using modified Otsu’s binarization technique in image processing. Pramana Res J 9(4):862–867. ISSN: 2249-2976
Acknowledgements
We would like to say deep thanks to the honorable advisers and research coordinator for their calm guidance, keen encouragement and valuable critiques for this research work. Also, we would like to extend our gratitude to Daffodil Institute of IT (DIIT) for offering us the required resources in the running program. Finally, we are delighted to say thank our family, for their great support and inspiration in all respects of our study.
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Rahman, S., Trisha, T.A., Imran Hossain Imu, M. (2022). Automated Vehicle License Plate Recognition System: An Adaptive Approach Using Digital Image Processing. In: Aurelia, S., Hiremath, S.S., Subramanian, K., Biswas, S.K. (eds) Sustainable Advanced Computing. Lecture Notes in Electrical Engineering, vol 840. Springer, Singapore. https://doi.org/10.1007/978-981-16-9012-9_25
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DOI: https://doi.org/10.1007/978-981-16-9012-9_25
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