DESIGN AND EVALUATE LICENSE PLATE DETECTION SYSTEM BASED ON SEGMENTATION

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 on the basis of 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 voting classifier is used for detecting the number plates of cars. For the purpose of voting classification, we have used a unique combination of classifiers. The voting 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. The proposed algorithm gives accuracy up to 95 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.