A kNN-based approach for the machine vision of character recognition of license plate numbers

• Main Authors: Quiros, Ana Riza F., Bedruz, Rhen Anjerome, Uy, Aaron Christian P., Abad, Alexander C., Bandala, Argel A., Dadios, Elmer P., Fernando, Arvin
• Format: text
• Published: Animo Repository 2017
• Subjects: Automobile license plates; Optical character recognition; Computer vision; Nearest neighbor analysis (Statistics); Neural networks (Computer science)
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2600
• Institution: De La Salle University

© 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.