Myanmar paper currency recognition using GLCM and k-NN

Paper currency recognition depends on the currency note characteristics of a particular country. And the features extraction directly affects the recognition ability. Paper currency recognition is one of the important applications of pattern recognition. This paper aims to present a model for automatic classification of currency notes using k-Nearest Neighbor (k-NN) classifier that is the most important and simplest method in pattern recognition. The proposed model is based on textural feature such as Gray Level Co-occurrence Matrix (GLCM). The recognition system is composed of four parts. The skew correction of rotated image is first. The captured image is second preprocessing and the third part is extracting its features by using GLCM. The last one is recognition, in which the core is k-Nearest Neighbor classifier. Experimental results are presented on a dataset of 500 images consisting of 5 classes of currency notes which are 100 Kyat, 200 Kyat, 500 Kyat, 1000 Kyat, and 5000 Kyat notes. It is shown that a good performance can be achieved using k-NN classifier algorithm. The recognition system presented in this paper indicates that the proposed approach is one of the most effective strategies of identifying currency pattern to read its face value.