License Plate Detection Using a Proposed Ensemble Learner Classifier

Authors

  • Shubham Anand, Prof. S. Indu

Abstract

A continual upsurge in the volume of vehicles has been noticed over the past few decades with
the increase in population all over the world. Therefore, tracking of vehicles depending upon the number
plates is crucial to guarantee the control of vehicular traffic in competent manner. The vehicles can be
detected based on their tags with the help of a new image processing-based technology referred as ANPR
(Automatic Number Plate Recognition) the expertise is ahead of time ubiquity to ensure security and traffic
management. This system makes use of computer vision approach for extracting information regarding the
abnormal state from a digital image using a computer. Almost all number plate localization algorithms
combine many processes that result in a long computational time. Most of the image details are lost or image
quality gets degraded as a result of complex, noisy content in images. The non-consistency of processes
cause degradation which in turn affects the image quality. The car number plate detection has many stages.
In this research work, technique of ensemble classifier is used for detecting the number plates of cars. For
the purpose of classification using a majority voting from a unique combination of two classifier. The
ensemble classification proposed in this research work for the number plate detection is the combination of
SVM and random forest classifier. The MATLAB and/or GNU Octave has been used for the evaluation of
the proposed model. The efficiency of new algorithmic approach is examined with respect to accuracy,
precision and recall. Further, the run time has been taken into consideration. The proposed algorithm gives
accuracy up to 96 percent for the car number plate detection. Similar, observation with the Precision and the
Recall that comes out to be 95.81 percent and 95.45 percent respectively. With total run/execution time less
than 11.5 seconds per sample image.

Published

2020-03-25

Issue

Section

Articles