A kNN-based approach for the machine vision of character recognition of license plate numbers
College
Gokongwei College of Engineering
Department/Unit
Manufacturing Engineering and Management
Document Type
Conference Proceeding
Source Title
IEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume
2017-December
First Page
1081
Last Page
1086
Publication Date
12-19-2017
Abstract
© 2017 IEEE. This research proposes to automate the plate recognition process by installing an IP camera on a road and analyzing the video-feed to capture the vehicles along that road. The contours of the characters in a given plate image are detected, violated and isolated from the parent image. This results to segmented characters. Each of the characters are identified using a k nearest neighbors (kNN) algorithm. The kNN algorithm was trained using different sets of training data containing 36 characters each. The algorithm was tested on the previously segmented characters. The simulations show that an accuracy of 87.43% was achieved for the plate recognition algorithm using kNN at k = 1. Compared against existing character recognition techniques such as artificial neural networks (ANN), the difference in the accuracy is minimal. Moreover, the average processing time was 0.034 s.
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Digitial Object Identifier (DOI)
10.1109/TENCON.2017.8228018
Recommended Citation
Quiros, A. F., Bedruz, R., Uy, A. P., Abad, A. C., Bandala, A. A., Dadios, E. P., & Fernando, A. (2017). A kNN-based approach for the machine vision of character recognition of license plate numbers. IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2017-December, 1081-1086. https://doi.org/10.1109/TENCON.2017.8228018
Keywords
Automobile license plates; Optical character recognition; Computer vision; Nearest neighbor analysis (Statistics); Neural networks (Computer science)
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