Automatic Number Plate Recognition using Canny Edge Detector, Sliding Concentric Windows and Linear Discriminant Analysis

Abstract

This paper presents a novel methodology of detection and recognizing characters in number plates in vehicles using Canny Edge Detector, Sliding Concentric Windows and Linear Discriminant Analysis. This framework of number plate recognition plays a vital role in parking places, toll gates, traffic monitoring, security control etc. This framework is implemented as a four step process. In the first step, the accepted image is preprocessed using Canny Edge Detector. This is a multi-stage algorithm that extracts wide variety of edges even in a noisy environment. The second step follows Sliding Concentric Windows (SCWs) for plate detection. License plate is extracted based on the vertical and horizontal position histogram and then morphological operations such as dilation and erosion are applied to get the right candidate plate region. The third step is character segmentation using Connected Component Analysis (CCA). Final step is for character recognition and it uses Linear Discriminant Analysis (LDA) using Local Binary Pattern (LBP) features. This work is implemented on the UFPR-ALPR Dataset and the experimental results shows the recognition accuracy of 88.5%.