scholarly journals An Efficient Method of Vehicle License Plate Recognition Based on Sliding Concentric Windows and Artificial Neural Network

2012 ◽  
Vol 4 ◽  
pp. 812-819 ◽  
Author(s):  
Kaushik Deb ◽  
Ibrahim Khan ◽  
Anik Saha ◽  
Kang-Hyun Jo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Efficient Method ◽  
License Plate ◽  
License Plate Recognition ◽  
Artificial Neural ◽  
Vehicle License Plate Recognition

scholarly journals Developing Arabic License Plate Recognition System Using Artificial Neural Network and Canny Edge Detection

2020 ◽  
Vol 17 (3) ◽  
pp. 0909
Author(s):  
Bydaa Ali Hussain ◽  
Mohammed Sadoon Hathal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Edge Detection ◽  
Recognition System ◽  
Detection Algorithm ◽  
License Plate ◽  
License Plate Recognition ◽  
Canny Edge Detection ◽  
Canny Edge ◽  
Artificial Neural

            In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the roads in all the sections of the country. Arabic 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 proposed system consists 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 identify and detect the vehicle license plate characters, and hence the results are displayed as a text on GUI. The proposed system successfully detects LP and recognizes multi-style Arabic characters with rates of 96% and 97.872% respectively under different conditions.

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