Analysis of Fluorescent Paper Pulps for Detecting Counterfeit Indian Paper Money

摘要

The paper itself forms an important security feature for many security paper documents. This work attempts to develop a machine assisted tool for authenticating the paper of a security document. Image processing and pattern recognition principles form the basis of this automatic method. Paper pulps play a crucial role in characterizing a paper material. These pulps are visible in the UV scanned image of the document. Therefore, the pulps are first identified in the UV scanned image. This identification is done by borrowing ideas from rice grain detection method. Once the pulps are detected, shape and color features are extracted from them. Paper pulps coming from fake documents are significantly different from those of genuine documents in their shapes and colors. Using the shape and color features, a multilayer back propagation neural network is used to discriminate paper pulps as genuine or fake. The proposed method is tested with Indian banknote samples. Experiment shows that consideration of paper pulps is one of the crucial tests for authenticating paper money.