Paper Title
First Order Statistics And Glcm Based Feature Extraction For Recognition Of Myanmar Paper Currency

Paper currency recognition is one of the important applications of pattern recognition. A paper currency recognition system has a wide range of applications such as self receiver machines for automated teller machines and automatic good-selling machines. The paper currency recognition is significant for a number of reasons. a) They become old early than coins; b) The possibility of joining broken currency is greater than that of coin currency; c) Coin currency is restricted to smaller range. In this paper, Myanmar paper currency recognition system based on First Order Statistics, Gray Level Co-occurrence Matrix (GLCM), and k-Nearest Neighbor (k-NN) is presented. Image processing is the most popular and effective method of paper currency recognition. Image processing based paper currency recognition technique consists of few basis steps like image acquisition, its preprocessing, median filter used to remove noise, feature extraction using first order statistics and GLCM, and finally recognition of the currency using k-NN classification. Index Terms- Currency Recognition, Gray Level Co-occurrence Matrix (GLCM), Image Processing, k-Nearest Neighbor (k-NN)