Abstract
In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the road in all the sections of the country. Vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the developing system is consist of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny Edge detection algorithm, Connect Component Analysis (CCA) have been exploited for segmenting characters. Finally, a Multi-Layer Perceptron Artificial Neural Network (MLPANN) model is utilized to recognize and detect the vehicle license plate characters, and hence the results are displayed as a text on GUI. The proposed system successfully identified and recognized multi_style Iraqi license plates using different image situations and it was evaluated based on different metrics performance, achieving an overall system performance of 91.99%. This results shows the effectiveness of the proposed method compared with other existing methods, whose average recognition rate is 86% and the average processing time of one image is 0.242s which proves the practicality of the proposed method.
Highlights
License Plate Recognition System (LPRS) is a significant application in the computer intelligent transportation field; It exploits digital image processing and detection techniques
The motivations of this work is to design and implement Iraqi license plate system which is very important for enforcing traffic law, controlling security in restricted area and identifying vehicles in unattended parking zone, so the main contribution of this work is to introduce an enhanced version of Iraqi LPRS based on a hybrid model of canny edge detection and Multi Layer Perceptron (MLP) artificial neural network using 50 images from Iraq natural scenes where taken under different conditions
Localization process is the procedure of finding the section or region in an image that contain LP, to present the algorithm for license plate localization the color image must convert to gray image canny edge detection algorithm is present, in gray scale image, an edge is define as disjoint in gray level values to formula main object boundaries, connect edge to rectangle based on morphology operation, the license plate located by using geometrical feature for license plate
Summary
License Plate Recognition System (LPRS) is a significant application in the computer intelligent transportation field; It exploits digital image processing and detection techniques. The motivations of this work is to design and implement Iraqi license plate system which is very important for enforcing traffic law, controlling security in restricted area and identifying vehicles in unattended parking zone, so the main contribution of this work is to introduce an enhanced version of Iraqi LPRS based on a hybrid model of canny edge detection and Multi Layer Perceptron (MLP) artificial neural network using 50 images from Iraq natural scenes where taken under different conditions. The recognition phase, we can obtain the plate number as a text accomplished by a Multi-Layer Perceptron Artificial Neural Network (MLPANN) on segment characters to recognize it, Artificial Neural Network (ANN) is an image processing technique that is influenced by the identical way of biological nervous system, similar to the brain system, that get more accuracy recognition rates with faster time recognition (Hana M A, et al, 2017, Ibrahim El K, et al, 2015, Bhara B, et al, 2013). Iraq LP has been used for the proposed system which have three styles written in Arabic language and each style have different size and design (Safaa S O and Jumana A J., 2017)
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