Detection of Fake Currency Using Machine Learning

Counterfeiting of currency is a growing problem due to advancements in color printing technology, impacting economies worldwide. Fake money is often used for criminal activities, including terrorism. Research indicates that developing countries, like India, are particularly affected. One solution proposed involves analyzing the image of counterfeit currency through pre-processing steps such as converting from RGB to grayscale. This approach aims to tackle the issue of counterfeit currency circulation effectively. After converting the text, the image is divided into sections, its characteristics are assessed, relationships are identified, and a decision is made to determine if the image is authentic or counterfeit. Counterfeit currency continues to be a notable problem. Financial institutions and other establishments have the necessary resources to confirm the legitimacy of money. However, the average person does not have access to such equipment, making it essential to have counterfeit money detection software that can be used by anyone. This study achieves a maximum accuracy of 81% when analyzing 50 Indian currency notes with a denomination of 2000 rupees. This initiative offers a comprehensive overview of a counterfeit detection system that is accessible to the general public. The proposed system uses image processing to distinguish real currency from counterfeit money. The entire software has been developed using the Python programming language. Key Words: Fake Currency, Counterfeit, Denomination, Python, Machine Learning,