Zernike Moment Based Feature Extraction for Classification of Myanmar Paper Currencies
Khin Nwe Oo, Anilkumar Kothalil Gopalkrishnan · 2018
The aim of this research is to classify the front side (observe) and back side (reserve) of Myanmar currencies (kyats) in three denominations. The system consists of binary image conversion, noise removal, feature extraction, and recognition. Zernike moment is used for feature extraction. The features from the top left, top right, bottom left and bottom right of the currency are extracted. The classification of the correct currency using the k -nearest neighbor algorithm based on the data set generated by Zernike Moment. The accuracy is measured based on the confusion matrix. The experimental results suggested that the approach is sufficiently effective and efficient in classifying the paper currencies.