An Algorithm for License Plate Recognition Using Radial Basis Function Neural Network

Abstract

Based on the sharing features of a variety of license plates (LP), the vertical edge was first detected by Sobel edge detector. Then, some approaches were adopted to remove the invalid edge regarding the characteristics of edge grayscale jump and edge density, so that the regions having features of LP were preserved. Next, by horizontal and vertical projections and mathematical morphology (MM) operation, the LP region was searched. Then, color-reversing judgement was conducted by color analysis, and binarization was done based on core region in LP. Afterward, characters were segmented by means of prior knowledge and connected components analysis, and character recognition was conducted based on radial basis function (RBF) neural network. With abundant samples verified in dark hours and daytime under real conditions, the experiment indicates that it is feasible to adopt this algorithm in license plate recognition system (LPRS) to achieve accuracy.