Support Vector Machine Method to Reduce the Execution Time of Vehicle Plate Recognition System

Abstract

This research aims to create a vehicle plate detection and recognition system with Cascade Classifier, Support Vector Machine (SVM) and Optical Character Recognition (OCR). Cascade Classifier with Local Binary Patterns (LBP)descriptor is used todetectthe carlicence plate (Coarse Location). SVM is used to reduce plate candidate detection error andthe execution time. Optical Character Recognition (OCR) is used to recognize characters in plates. The system test is performed using 19 video data of moving vehicles at night and rain conditions. Each video has a duration of 30 seconds and contains 4-10 cars per video. The testing results reduce the execution time of vehicle plate recognition systemreached60% with the average accuracy of plate recognition is 61.94%.