License Plate Detection and Recognition: An Empirical Study

Abstract

Vehicle License Plate Detection and Recognition has become critical to traffic, security and surveillance applications. This contribution aims to implement and evaluate different techniques for License Plate Detection and Recognition in order to improve their accuracy. This work addresses various problems in detection such as adverse weather, illumination change and poor quality of captured images. After detecting the license plate location in an image the next challenge is to recognize each letter and digit. In this work three different approaches have been investigated to find which one performs best. Here, characters are classified through template matching, multi-class SVM, and convolutional neural network. The performance was measured empirically, with 36 classes each containing 400 images per class used for training and testing. For each algorithm empirical accuracy was assessed.