Classification and Detection of Banknotes using Machine Learning

On the basis of the look, people can easily differentiate banknotes and coin denominations. The coin currencies can be identified visually impaired people based on touch, but the note currencies cannot be identified easily as it has similar texture and appearance, it can be challenging for visually challenged people to distinguish the currencies. Demonetization has boosted the availability of fake cash in recent years. People face difficulty in distinguishing between real and fake banknotes because they are unaware of the security elements utilized in modern currencies. Additionally, these fake cash mislead persons who don't have proper vision. So, it becomes important to identify the denominations and detect fake and real banknotes in-order to avoid the problems caused due to these currencies or banknotes. This issue highlights the requirement for an accurate banknote identification model. By spotting the counterfeit currency, inflation and currency devaluation can be stopped. The suggested model aims to identify the denomination and categorize if a money note is real or fraudulent. The banknote denomination is determined using the machine learning algorithms like Support vector machines (SVM), decision trees classification (DTC), random forest classification (RFC), and K-NN (K-Nearest Neighbors) and the currency's authenticity is determined using a deep learning method called AlexNet.