Abstract
Automatic License Plate Detection and Recognition (ALPR) is one of the most significant technologies in intelligent transportation and surveillance across the world. It has many challenges because it affects by many parameters such as the country’s layout, colors, language, fonts, and several environmen-tal conditions so, there isn’t a consolidated ALPR system for all countries. Many ALPR methods have been proposed based on traditional image processing and machine learning algorithms since there aren’t enough datasets, particularly in the Arabic language. In this paper, we proposed a real-time ALPR system for the Egyptian license plate (LP) detection and recognition using Tiny-YOLOV3. It consists of two deep convolutional neural networks. The experimental results in the first available publicly Egyptian Automatic License Plate (EALPR) dataset show the proposed system is more robust in detecting and recognizing the Egyptian license plates and gives mean average precision values of 97.89% and 92.46% for LP detection and character recognition, respectively.
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