I.J. Intelligent Systems and Applications, 2023, 4, 37-52 Published Online on August 8, 2023 by MECS Press (http://www.mecs-press.org/) DOI: 10.5815/ijisa.2023.04.04 This work is open access and licensed under the Creative Commons CC BY 4.0 License. Volume 15 (2023), Issue 4 Design of Automatic Number Plate Recognition System for Yemeni Vehicles with Support Vector Machine Farhan M. Nashwan Department of Electrical Engineering, Ibb University, Ibb City, Yemen E-mail: far_nash@hotmail.com Khaled A. M. Al Soufy* Department of Electrical Engineering, Ibb University, Ibb City, Yemen E-mail: kalsoufi@ibbuniv.edu.ye ORCID iD: https://orcid.org/0000-0002-1224-1405 *Corresponding Author Nagi H. Al-Ashwal Department of Electrical Engineering, Ibb University, Ibb City, Yemen E-mail: nlashwal@yahoo.com ORCID iD: https://orcid.org/0009-0005-9566-1956 Majed A. Al-Badany Department of Electrical Engineering, Ibb University, Ibb City, Yemen E-mail: magedalbadany1@gmail.com Received: 24 March 2023; Revised: 15 May 2023; Accepted: 22 June 2023; Published: 08 August 2023 Abstract: 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. Index Terms: ANPR, Image Segmentation, Digit Recognition, SVM. 1. Introduction Various recognition systems have been developed to be used in several security and traffic applications, such as airport parking, maintaining law enforcement on public roads, access and border control, or tracking of stolen cars. Automatic Number Plate Recognition (ANPR) systems have become an effective and promising research topic computer vision. ANPR is a technique applied to observe and recognize vehicle number plate characters from static and/or moving vehicle images [1,2,3,4,6]. Many machine-learning approaches such as Deep Learning (DL), Support Vector Machine (SVM), and Neural Network (NN) methods, are used to recognize and detect vehicle plate licenses [7]. Vehicles traveling on public roadways in Yemen and many other countries are required by law to carry a clearly visible placard with a unique identifier registered with the local government. This placard, most commonly called a license plate (LP), can contain various symbols, letters, numbers, logos, etc. based on local government regulations and the class of the vehicle. According to the annual report of the Traffic Police Department [8], the registration of vehicles’