Myanmar Paper Currency Recognition Using GLCM and k-NN
Files (Excerpt)
Issued Date
2016-01
Genre
Language
eng
File Type
application/pdf
No. of Pages/File Size
6 pages
Citation
Proceedings of the 2nd Asian Conference on Defence Technology, – IEEE XPlore, pp. 67-72
Title
Myanmar Paper Currency Recognition Using GLCM and k-NN
Abstract
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.