Inspection of water mark on currency note by using correlation mapping and neural network

This paper proposes a method of inspecting water mark on currency note by using correlation mapping and backpropagation neural network. In this method, the location of water mark is detected by correlation mapping with the edge on reference image. To certify the water mark, the edge information from the shadow of water mark is inputted to backpropagation neural network and it is classified into the currency note or the copy. In the experiment, five samples each of five types (B20,B50,B100,B500,B1000) of Thai currency note were trained, and 20 samples of each were tested. The results reveal that the currency notes are inspected approximately with 99.00%, accuracy of recognizable type of currency note and 100.00% by using all of the edge information of currency note and the copies were rejected.