The Open Artificial Intelligence Journal




(Discontinued)

ISSN: 1874-0618 ― Volume 6, 2020
RESEARCH ARTICLE

An Efficient Approach for Vehicle Number Plate Recognition in Pakistan



Saif Ur Rehman1, *, Moiz Ahmad1, Asif Nawaz1, Tariq Ali1
1 University Institute of Information Technology, PMAS Arid University, Rawalpindi, Pakistan

Abstract

Introduction:

Recognition of Vehicle License Number Plates (VLNP) is an important task. It is valuable in numerous applications, such as entrance admission, security, parking control, road traffic control, and speed control. An ANPR (Automatic Number Plate Recognition) is a system in which the image of the vehicle is captured through high definition cameras. The image is then used to detect vehicles of any type (car, van, bus, truck, and bike, etc.), its’ color (white, black, blue, etc.), and its’ model (Toyota Corolla, Honda Civic etc.). Furthermore, this image is processed using segmentation and OCR techniques to get the vehicle registration number in form of characters. Once the required information is extracted from VLNP, this information is sent to the control center for further processing.

Aim:

ANPR is a challenging problem, especially when the number plates have varying sizes, the number of lines, fonts, background diversity, etc. Different ANPR systems have been suggested for different countries, including Iran, Malaysia, and France. However, only a limited work exists for Pakistan vehicles. Therefore, in this study, we aim to propose a novel ANPR framework for Pakistan VLNP recognition.

Methods:

The proposed ANPR system functions in three different steps: (i) - Number Plate Localization (NPL); (ii)- Character Segmentation (CS); and (iii)- Optical Character Recognition (OCR), involving template-matching mechanism. The proposed ANPR approach scans the number plate and instantly checks against database records of vehicles of interest. It can further extract the real=time information of driver and vehicle, for instance, license of the driver and token taxes of vehicles are paid or not, etc.

Results:

Finally, the proposed ANPR system has been evaluated on several real-time images from various formats of number plates practiced in Pakistan territory. In addition to this, the proposed ANPR system has been compared with the existing ANPR systems proposed specifically for Pakistani licensed number plates.

Conclusion:

The proposed ANPR Model has both time and money-saving profit for law enforcement agencies and private organizations for improving homeland security. There is a need to expand the types of vehicles that can be detected: trucks, buses, scooters, bikes. This technology can be further improved to detect the crashed vehicle’s number plate in an accident and alert the closest hospital and police station about the accident, thus saving lives.

Keywords: Vehicle license plate recognition, Image segmentation, Optical character recognition, Template matching, Artificial Intelligence, Machine learning.


Article Information


Identifiers and Pagination:

Year: 2020
Volume: 6
First Page: 12
Last Page: 21
Publisher Id: TOAIJ-6-12
DOI: 10.2174/1874061802006010012

Article History:

Received Date: 21/03/2020
Revision Received Date: 17/04/2020
Acceptance Date: 02/05/2020
Electronic publication date: 18/06/2020
Collection year: 2020

© 2020 Rehman et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


* Address correspondence to this author at the University Institute of Information Technology, PMAS Arid University, Rawalpindi, Pakistan; Tel: +92-343-580-2355; Fax: +92-51-9292195; E-mail: Saif@uaa.edu.pk



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