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Title |
Detection and Recognition of Vehicle License Plates using Deep Learning in Video Surveillance |
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Author |
Muhammad Umer Farooq, Saad Ahmed, Mustafa Latif, Danish Jawaid, Muhammad Zofeen Khan, Yahya Khan |
| Citation |
Vol. 22 No. 11 pp. 121-126 |
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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. |
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Keywords |
YOLOv5; License plate; OCR, Image segmentation |
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