Detecting Counterfeit Indian Currency A Comparative Analysis of Machine Learning Algorithms and Image Processing

The degree of secured currency is one of the key areas that makes its contribution in solving the problem of economic insecurity, highlights the most important. Even as it can be quite strained to actually identify which precise time frame this fake process was prepared in, complex fakes render the use of validation techniques quite challenging. This paper has aimed at displaying and evaluating the overall outcome of the different strategies of image, and the acquired classifiers in the identification of the fake FI currency. The following are the subsequent of the other operation that was applied in order to achieve the objectives of the intended work; Image capture This is whereby the images to be used are taken by the appropriate camera or scanner. Others used include the Naïve Bayes, Support Vector Classifier and Gradient Descent and Artificial Neural Network in this paper. As in the conclusion, with the help of the mathematical formulation, it can be confirmed that in fact, within an experiment, Neural Networks do have a higher level of optimization, much more than an average image processing if it is provoked to identify fake notes. In other words if used and implemented with the best of the machine learning approach and best of the image processing method then solitaire for detection of the counterfeit currency can be made more dependable than all the combined methods stated above. The objective of this study is to build and compare the performance of different machine learning algorithms and image processing methodologies for the identification of fake Indian currency notes.