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.

html

Digitial Object Identifier (DOI)

10.1109/TENCON.2017.8228018

Keywords

Automobile license plates; Optical character recognition; Computer vision; Nearest neighbor analysis (Statistics); Neural networks (Computer science)

Upload File

wf_no

This document is currently not available here.

Share

COinS