This paper is published in Volume-7, Issue-2, 2021
Area
Electronic And Communication
Author
Shahid Yousuf, Dr. Rajat Joshi
Org/Univ
Adesh Institute of Technology, Gharuan, Punjab, India
Pub. Date
14 April, 2021
Paper ID
V7I2-1363
Publisher
Keywords
Random forest, WCA, Electronic Toll-Collection, Automatic Number Plate Recognition

Citationsacebook

IEEE
Shahid Yousuf, Dr. Rajat Joshi. Improved vehicle number plate detection with bounded segmentation and random forest, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Shahid Yousuf, Dr. Rajat Joshi (2021). Improved vehicle number plate detection with bounded segmentation and random forest. International Journal of Advance Research, Ideas and Innovations in Technology, 7(2) www.IJARIIT.com.

MLA
Shahid Yousuf, Dr. Rajat Joshi. "Improved vehicle number plate detection with bounded segmentation and random forest." International Journal of Advance Research, Ideas and Innovations in Technology 7.2 (2021). www.IJARIIT.com.

Abstract

Automatic Number Plate Recognition System is the identification system of vehicles. It is an image processing technology used to identify the vehicles only by their license plates. Automatic Number Plate Recognition ANPR plays am major role in management of parking areas, and surveillance of illegally parked vehicles. Since every vehicle has a unique number plate so it can be identified by its number plate. The classification is utilized for the electronic toll-collection system (ETC) and to display available parking spaces to vehicles. The identification is also employed for managing parking facilities, monitoring and analysis of traveling time, and security systems such as observation of stolen vehicles and monitoring of unauthorized vehicles entering private areas. Number plate recognition is realized by acquiring images of either the front or the rear of vehicles with cameras and then by image processing to identify license plates. In this paper proposed approach extract texture features and optimize by WCA then classified by Random forest.