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Title	Smart Parking System Using Yolov3 Deep Learning Model
ID_Doc	51292
Authors	Tater R.; Nagrath P.; Mishra J.; de Albuquerque V.H.C.; Menezes J.W.M.
Year	2023
Published	Lecture Notes in Networks and Systems, 787 LNNS
DOI	http://dx.doi.org/10.1007/978-981-99-6550-2_30
Abstract	When it comes to vehicle surveillance, the Smart Parking System is considered indispensable, and it is now making its mark in the parking management sector. The errors caused by manual entry of vehicle registration information are absolutely eliminated. The process is made absolutely smooth and safe by the Smart Parking System. This not only stores reliable vehicle registration data in the database, but it also verifies the vehicle at exit points automatically. The Automatic Number Plate Recognition (ANPR) system is a crucial component of smart cities as it uses image processing and optical character recognition (OCR) technology to read vehicle number plates. ANPR enables traffic control and law enforcement through an automated, fast, reliable, and robust vehicle plate recognition system. This paper proposes an enhanced OCR-based plate detection approach that utilizes YOLOv3 deep learning model and an object-based dataset trained by convolutional neural network (CNN) to detect alphanumeric data from the identified license plate. The project will produce a Dataframe containing vehicle’s registration details, entry time, exit time, and fees for the total duration of parking. To boost accuracy, a blended algorithm for license plate detection and recognition is proposed and compared to current methodologies. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Author Keywords	Automatic number plate recognition (ANPR); Convolutional neural networks (CNN); Optical character recognition (OCR); Pytesseract; YOLOv3

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