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Reseach Article

A Machine Learning Algorithm for Automatic Number Plate Recognition

by Reshu Kumari, Surya Prakash Sharma
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 174 - Number 1
Year of Publication: 2017
Authors: Reshu Kumari, Surya Prakash Sharma
10.5120/ijca2017915297

Reshu Kumari, Surya Prakash Sharma . A Machine Learning Algorithm for Automatic Number Plate Recognition. International Journal of Computer Applications. 174, 1 ( Sep 2017), 6-9. DOI=10.5120/ijca2017915297

@article{ 10.5120/ijca2017915297,
author = { Reshu Kumari, Surya Prakash Sharma },
title = { A Machine Learning Algorithm for Automatic Number Plate Recognition },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2017 },
volume = { 174 },
number = { 1 },
month = { Sep },
year = { 2017 },
issn = { 0975-8887 },
pages = { 6-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume174/number1/28369-2017915297/ },
doi = { 10.5120/ijca2017915297 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:20:58.703617+05:30
%A Reshu Kumari
%A Surya Prakash Sharma
%T A Machine Learning Algorithm for Automatic Number Plate Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 174
%N 1
%P 6-9
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Identification of cars is a demanding function for surveillance and control systems. People can recognize automobiles through license plates which consist of alphabets and numbers. We can use the uniqueness of a combination of characters in license plates for many purposes. For example, an arrest of a suspect’s vehicle, imposing parking violation fines, and entrance authentication are possible. However, it is a labor intensive job to identify all passing or parked vehicles’ license plates. This paper presents a training based approach for the recognition of vehicle number plate. The whole process has been divided into three stages i.e. capturing the image, plate localization and recognition of digits over the plate. HOG features have been used for the training purpose and Support Vector Machine is employed for the classification purpose yielding in more than 99% accuracy while recognition. The algorithm has been tested over more than 100 images.

References
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Index Terms

Computer Science
Information Sciences

Keywords

ANPR SVM HOG Features Character Recognition.