A live-video automatic number plate recognition (ANPR) system using convolutional neural network (CNN) with data labelling on an android smartphone

Abd Gani, Shamsul Fakhar and Miskon, Muhammad Fahmi and Hamzah, Rostam Affendi and Mohamood, Nadzrie and Manap, Zahariah and Zulkifli, Mohamad Fakhri and Md Ali Shah, M. A. S. (2021) A live-video automatic number plate recognition (ANPR) system using convolutional neural network (CNN) with data labelling on an android smartphone. International Journal of Emerging Technology and Advanced Engineering, 11 (10). pp. 88-95. ISSN 2250-2459

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Abstract

Automatic Number Plate Recognition (ANPR) combines electronic hardware and complex computer vision software algorithms to recognize the characters on vehicle license plate numbers. Many researchers have proposed and implemented ANPR for various applications such as law enforcement and security, access control, border access, tracking stolen vehicles, tracking traffic violations, and parking management system. This paper discusses a live-video ANPR system using CNN developed on an Android smartphone embedded with a camera with limited resolution and limited processing power based on Malaysian license plate standards. In terms of system performance, in an ideal outdoor environment with good lighting and direct or slightly skewed camera angle, the recognition works perfectly with a computational time of 0.635 seconds. However, this performance is affected by poor lighting, extremely skewed angle of license plates, and fast vehicle movement.

Item Type:	Article
Uncontrolled Keywords:	Computer vision, Image processing, Automatic number plate recognition, Vehicle identification
Divisions:	Faculty of Electrical and Electronic Engineering Technology
Depositing User:	Sabariah Ismail
Date Deposited:	26 Jun 2024 11:27
Last Modified:	26 Jun 2024 11:27
URI:	http://eprints.utem.edu.my/id/eprint/26825
Statistic Details:	View Download Statistic

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