Real Time Vehicle License Plate Recognition Based on 2D Haar Discrete Wavelet Transform

This thesis is to present a new approach for license-plate recognition using 2D Haar Discrete Wavelet Transform (HDWT) and Artificial neural Network. This thesis consists of three main parts. The first part is to locate and extract the license-plate in an image. The second part is to train of the license-plate. The third part is to real time scan recognize of the license-plate. Traditional license plate recognition system design complexity. The paper present is a vehicle license plate after 2D Haar Discrete Wavelet Transform three transforms, select only after the third conversion coefficients of low-frequency part of the image pixels, image pixels into one-sixty fourth, thus reducing the number of scanning image pixels, increasing rapid implementation of recognition work and the memory usage. This article is to directly scan for license plate recognition, without recognition of the individual characters. This new approach is a real time recognition, experimental results of license plate recognition rates can be as high as 95.33%.