Character Segmentation for Automatic Vehicle License Plate Recognition Based on Fast K-Means Clustering

Automatic vehicle license plate recognition (AVLPR) system is one of application for transportation area under intelligent transport system. This system helps in monitor and identify the vehicle by reading the vehicles license plate numbers and recognize the plate characters automatically. However,...

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Bibliographic Details
Published in:	2020 IEEE 10th International Conference on System Engineering and Technology (ICSET) pp. 228 - 233
Main Authors:	Ariff, F. N. M., Nasir, A. S. A., Jaafar, H., Zulkifli, A. N.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 09-11-2020
Subjects:	Character recognition clustering algorithm Clustering algorithms Gabor filters Gray-scale Image recognition Image segmentation License plate recognition optical character recognition
Online Access:	Get full text
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Summary:	Automatic vehicle license plate recognition (AVLPR) system is one of application for transportation area under intelligent transport system. This system helps in monitor and identify the vehicle by reading the vehicles license plate numbers and recognize the plate characters automatically. However, various factors such as diversity of plate character viewpoint, shape, format and unstable light conditions at the time of image acquisition were obtained, have challenged the system to segment and recognize the characters. Therefore, this paper, presents an effective procedure approached based on fast k-mean (FKM) clustering. FKM approached have an ability to shortening the time of the image cluster centers process consumed. In addition, the FKM algorithm also able to overcomes the cluster center re-processing problem when constantly added the image in huge quantities. The proposed procedure begins with enhancing the input image by using modified white patch and converted into grayscale image. A total of 100 of images has been tested for the segmentation process with clustering techniques approach used. Template matching is used to standardize the recognition results obtained. The highest achieved was 88.57% of average accuracy for FKM clustering technique compared to k-means clustering where it was only able to achieve an average accuracy of 85.78% and 86.14% for fuzzy c-means. Thus, this show that the most efficient, quicker and more useful algorithm goes to FKM rather than the algorithm for fuzzy c-means (FCM) and k-means (KM). Therefore, it is possible toward consider the proposed FKM clustering as an image segmentation method for segmenting license plate images.
ISSN:	2470-640X
DOI:	10.1109/ICSET51301.2020.9265387