IMAGE-BASED FRAUDULENT CURRENCY DETECTION

Abstract

This paper deals with the matter of identifying the currency that if the gi ven sample of currency is
fake. Different traditional strategies and methods are available for fake currency identification based on the
colors, wi dth, and serial numbers mentioned. In the advanced age of Computer science and high
computational methods, various machine learning algorithms are proposed by image processing that gi ves
99.9% accuracy for the fake identity of the currency. Detection and recognition methods over the algorithms
include entities like color, shape, paper wi dth, image filtering on the note. This paper proposes a method
for fake currency recognition using K-Nearest Neighbours followed by image processing. KNN has a high
accuracy for small data sets making it desirable to be used for the computer vision task. In this, the
banknote authentication dataset has been created with the high computational and mathematical strategies ,
which gi ve the correct data and information regarding the entities and features related to the currency.
Data processing and data Extraction is performed by implementing machine learning algorithms and image
processing to acquire the final result and accuracy.