Farhan M. Nashwan

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

E-mail: far_nash@hotmail.com


Research Interests: Optical Communication, Pattern Recognition, Data Compression


Farhan Mohammed Ali Nashwan has received his B.Sc and Master of Science at Electrical Engineering, Department of Electrical Engineering, Al-Mustansiriyah University, Baghdad, Iraq, in 1997 and 2001 respectively. He was working at Minster of communication: Installation & Maintenance for transmissions and rural communications systems, Installation & Maintenance Yemen Mobile systems (BSC & BTS stations). He has joined the teaching staff of the Department of Electrical Engineering, Faculty of Engineering, Ibb University, Yemen, in 2005 as a assistance lecturer. He has received his Ph.D. in Electronics & Electrical Communications Engineering, Cairo University, Egypt. He has joined the teaching staff of the Department of Electrical Engineering, Faculty of Engineering, Ibb University, Yemen, in 2014 as assistant Professor. He is working as a Dean of Faculty of Engineering, Ibb University. His research interest includes Digital Image Compression, Optical Character Recognition, Pattern 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: https://doi.org/10.5815/ijisa.2023.04.04, 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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