Voting Classification Method for Vehicle Number Plate Detection

The massive growth in the number 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. Most of the image details are lost or image quality gets degraded as a result of complex or noisy content in images. The nonconsistency of processes cause degradation which in turn affects the image quality. The car number plate detection has various phases which are pre-processing, feature extraction and classification. For the purpose of voting classification, we have used a unique combination of classifiers. The voting classification introduced in this work for the number plate detection is the combination of SVM and RF classification model. The MATLAB has been used for the evaluation of the proposed model. The performance of the proposed algorithm is analyzed with respect to accuracy, precision and recall. The proposed algorithm gives accuracy up to 95 percent for the car number plate detection in various conditions. Similar observations have been noticed with the Precision and the Recall that goes up to 91.81 percent and 95.45 percent respectively.