DEEP LEARNING BASED SMART LICENSE NUMBER PLATE RECOGNITION SYSTEM FOR MOVING CARS
Author(s):
Muahmamd Ali Ismail1, Hareem Arshad2, Tooba Siddiqui3, Ramlah Ahmed4, Javeria Shabbir5
1 Associate Professor, Department of Computer and Information Systems Engineering, NED University of Engineering and Technology, Pakistan, Ph. +92-(0)21-99261261x2371, Fax: +92(0)91-99261255, Email: maismail@neduet.edu.pk.
2 Software Engineer, Department of Computer and Information Systems Engineering, NED University of Engineering and Technology, Pakistan, Ph. +92-(0)21-99261261x2371, Fax: +92(0)91-99261255, Email: hareemarshad.m@gmail.com.
3 Analyst Software Engineer, Department of Computer and Information Systems Engineering, NED University of Engineering and Technology, Pakistan, Ph. +92-(0)21-99261261×2237, Fax: +92(0)91-99261255, Email: Tooba.siddiqui@yahoo.com.
4 Algorithm Analyst, Department of Computer and Information Systems Engineering, NED University of Engineering and Technology, Pakistan, Ph. +92-(0)21-99261261×2237, Fax: +92(0)91-99261255, Email: ramlahahmed26@gmail.com.
5 Research Assistant, Department of Computer and Information Systems Engineering, NED University of Engineering and Technology, Pakistan, Ph. +92-(0)21-99261261×2237, Fax: +92(0)91-99261255, Email: javeriashabbir47@gmail.com.
Volume:
Thematic Issue on Advances in Image and Video Processing
Pages:
47 - 55
Date:
May 2018
Abstract:
Traffic in metropolitan cities increases with an increase in per capita income, especially in developing countries. Proper regulation and monitoring of road traffic poses a challenge for smart cities. In addition, due to variation in the traffic systems worldwide adoption of a single system is difficult. This paper presents an intelligent car license number plate recognition system which is capable of identifying different designs and number plate formats. Deep learning algorithm has been employed to make the system intelligent, efficient and faster. The system has been tested for up to ninety percent accuracy using an experimental setup deployed at NED University of Engineering and Technology, Pakistan and can be implemented on large scale.