THE AUTOMATIC LICENSE PLATE RECOGNITION USING FEATURES EXTRACTION AND NEURAL NETWORKS
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%.
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