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Smart city article details

Title	Car Number Plate Detection Using Deep Learning
ID_Doc	13389
Authors	Kushwaha M.K.; Suseela G.
Year	2022
Published	2022 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2022
DOI	http://dx.doi.org/10.1109/ICDSAAI55433.2022.10028818
Abstract	The rapid growth of technology has led to several improvements in roadways, which has led to an increase in the number of automobiles on the roads. Monitoring and traffic management are therefore necessary and crucial. Due to population expansion, manual vehicle tracking is neither practical nor effective. It consumes Resources and Efforts in terms of time and hard work, still the results are not effective. Tracking vehicles will help in preventing vehicle misuse, locate drivers, and improve customer service and protection against thefts. Keeping track of individual vehicles has grown to be a very challenging undertaking as the automotive industry continues to grow dramatically every day. This study suggests the deployment of roadside surveillance cameras as part of an autonomous vehicle monitoring system for automobiles. License plate recognition systems are utilized for toll collection, parking fee, and residential entrance control in modern smart cities. In addition to being helpful in people's daily lives, these electronic technologies also give management access to secure and effective services. The suggested technique incorporates a successful method for identifying licence plates on automobiles. The suggested approach can be used to deal with number plates that have a noisy, poorly lit, cross-angled, non-standard font. With the use of character segmentation and a convolutional neural network (CNN)-based recognition model, this study provides a powerful deep learning-based Automatic License Plate Recognition (ALPR) model. The experimental finding yields a f1 score accuracy percentage of 94.94%. © 2022 IEEE.
Author Keywords	ALPR or ANPR; Character Recognition; Character Segmentation; CNN; Eroding; OKM-CNN

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