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Title: | Vehicle Number Plate Detection and Recognition Techniques: A Review |
Authors: | Parvin, Shahnaj Islam, Dr. Ezharul Rozario, Liton J. |
Keywords: | Number plate detection Number plate recognition Optical Character Recognition You Only Look Once (YOLO) Convolutional Neural Network Vehicle detection |
Issue Date: | 17-Mar-2021 |
Publisher: | ASTES |
Series/Report no.: | Volume 06 (02);ASTESJ_060249 |
Abstract: | Vehicle number plate detection and recognition is an integral part of the Intelligent Transport System (ITS) as every vehicle has a number plate as part of its identity. The quantity of vehicles on road is growing in the modern age, so numerous crimes are also increasing day by day. Almost every day the news of missing vehicles and accidents are perceived. Vehicles tracking is often required to investigate all these illegal activities. So, vehicle number plate identification, as well as recognition, is an active field of study. However, vehicle number plate identification has always been a challenging task for some reasons, for example, brightness changes, vehicle shadows, and non-uniform license plate character type, various styles, and environment color effects. In this review work, various state-of-the-art vehicle number plate detection, as well as recognition strategies, have been outlined on how researchers have experimented with these techniques, which methods have been developed or used, what datasets have been focused on, what kinds of characters have been recognized and how much progress have been achieved. Hopefully, for future research, this review would be very useful. |
URI: | http://dspace.aiub.edu:8080/jspui/handle/123456789/1843 |
Appears in Collections: | Publications: Journals |
Files in This Item:
File | Description | Size | Format | |
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Dspace_p2.docx | 4.66 MB | Microsoft Word XML | View/Open |
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