Majed A. Al-Badany

Work place: Department of Electrical Engineering, Ibb University, Ibb City, Yemen



Research Interests: Optical Communication, Image Processing


Majed Ahmed Abdu Thabet Al-Badan, has received his B.SC at Electrical Engineering, Department of Computer Engineering and Informatics In 2017 with an excellent percentage with honor, IBB University, IBB, Yemen. He has joined the teaching staff of the Department of Electrical Engineering, Faculty of Engineering, IBB University, Yemen since the period from 2017 to 2021. He is working as a Core Network Engineer, General Administration of Operation & Internet, Core Network Administration, Public Telecom Corporation, Yemen. His Research interests include Digital Image Processing, Optical Character Recognition.

Author Articles
Design of Automatic Number Plate Recognition System for Yemeni Vehicles with Support Vector Machine

By Farhan M. Nashwan Khaled A. M. Al Soufy Nagi H. Al-Ashwal Majed A. Al-Badany

DOI:, Pub. Date: 8 Aug. 2023

Automatic Number Plate Recognition (ANPR) is an important tool in the Intelligent Transport System (ITS). Plate features can be used to provide the identification of any vehicle as they help ensure effective law enforcement and security. However, this is a challenging problem, because of the diversity of plate formats, different scales, rotations and non-uniform illumination and other conditions during image acquisition. This work aims to design and implement an ANPR system specified for Yemeni vehicle plates. The proposed system involves several steps to detect, segment, and recognize Yemeni vehicle plate numbers. First, a dataset of images is manually collected. Then, the collected images undergo preprocessing, followed by plate extraction, digit segmentation, and feature extraction. Finally, the plate numbers are identified using Support Vector Machine (SVM). When designing the proposed system, all possible conditions that could affect the efficiency of the system were considered. The experimental results showed that the proposed system achieved 96.98% and 99.19% of the training and testing success rates respectively.

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