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http://dx.doi.org/10.22937/IJCSNS.2022.22.11.17

Detection and Recognition of Vehicle License Plates using Deep Learning in Video Surveillance  

Farooq, Muhammad Umer (Department of Computer Science and Information Technology, NED University of Engineering and Technology)
Ahmed, Saad (Department of Computer Science, IQRA University Karachi Pakistan)
Latif, Mustafa (Department of Software Engineering, NED University of Engineering and Technology)
Jawaid, Danish (Department of Computer Science and Information Technology, NED University of Engineering and Technology)
Khan, Muhammad Zofeen (Department of Computer Science and Information Technology, NED University of Engineering and Technology)
Khan, Yahya (Department of Computer Science and Information Technology, NED University of Engineering and Technology)
Publication Information
International Journal of Computer Science & Network Security / v.22, no.11, 2022 , pp. 121-126 More about this Journal
Abstract
The number of vehicles has increased exponentially over the past 20 years due to technological advancements. It is becoming almost impossible to manually control and manage the traffic in a city like Karachi. Without license plate recognition, traffic management is impossible. The Framework for License Plate Detection & Recognition to overcome these issues is proposed. License Plate Detection & Recognition is primarily performed in two steps. The first step is to accurately detect the license plate in the given image, and the second step is to successfully read and recognize each character of that license plate. Some of the most common algorithms used in the past are based on colour, texture, edge-detection and template matching. Nowadays, many researchers are proposing methods based on deep learning. This research proposes a framework for License Plate Detection & Recognition using a custom YOLOv5 Object Detector, image segmentation techniques, and Tesseract's optical character recognition OCR. The accuracy of this framework is 0.89.
Keywords
YOLOv5; License plate; OCR; Image segmentation;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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