الفهرس 
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Abstract
Automatic License Plate Recognition (ALPR) has become an integral part of the new
ecosystems to ensure safety and traffic management. It has many challenges because
it is affected by many parameters, such as the country’s layout, colors, language,
fonts, and several environmental conditions. So, there isn’t a unified ALPR system
for all countries. Many ALPR methods were based on traditional image process-
ing and machine learning algorithms since there aren’t enough datasets, particularly
in the Arabic language. We proposed a real-time ALPR system for Egyptian li-
cense plate (LP) detection and recognition using Tiny-YOLOV3. It consists of two
deep convolutional neural networks. Also, a large-scale dataset has been proposed,
namely Egyptian Automatic License Plate Recognition (EALPR), to address this is-
sue. Instaloader and selenium Web scraping techniques are used to capture photos
from social networking platforms. The YOLO detector is used for annotation after
cleaning and sanity tests. The total number of vehicles is 2,450, including different
locations, times (night and day), and backgrounds to guarantee the balance of the
dataset. The experimental results in the first publicly available EALPR dataset show
that the proposed system is more robust in detecting and recognizing the Egyptian
license plates and gives mean average precision (MAP) values of 97.89% for plate
detection and 92.46% for character recognition