THE AUTOMATIC LICENSE PLATE RECOGNITION USING FEATURES EXTRACTION AND NEURAL NETWORKS - ABDULJABBER, REEM Q.; SHAKIR, MOHAMMED A. (Journal of Duhok University, 2021)
The automatic license plate recognition (ALPR) system opens the trendy door to the researchers to think, discover techniques and reach to a result for its necessity. The important of the ALPR system is appeared in the transportation for many reasons such as parking, traffic violations and security. The aim of this paper is to suggest a scheme that will extract car number, country and province from the car images. The proposed scheme is based on digital image processing techniques and neural networks. The proposed algorithm is composite of preprocessing and recognition stages. The preprocessing stage includes: locate the car plate region, binarization, enhancement of the image quality, segment the image into the sub-images. The recognition stage will classify and recognize the segmented sub-images as numbers and characters. In this research, the localization is done through normal cross correlation method. The segmentation includes: segment the car plate into three regions, divide the number and separated character into individual and split the connected characters into separated characters are done through suggested algorithms. The recognition is accomplished using the back propagation neural network(BPNN). The recognizer operates on two sets of data. First set of data includes the whole pixels of the sub-images. The second set of data is based on 16 features extracted from the sub-images. A comparison between these two methods is made. The system is experienced on 99 images of Duhok and Erbil provinces, the environment work is done with MATLAB program. The percentage accuracy is: 100%,100% and 100% for the localization, distinguish and segmentation respectively. The recognition rate result for the first method is 94.5% and the second method is 91%.