Fake Currency Detection Using Pattern Recognition Algorithm

The creation of numerous counterfeit banknotes in recent years has had a detrimental effect on society and resulted in significant losses. Thus, it is now imperative to establish a technique for identifying fake cash. To combat this problem, the article uses a pattern recognition algorithm to provide a strong solution for fake currency identification. The aim is to create a precise and effective system that can differentiate between real currency and fake notes. In order to examine and distinguish between the complex characteristics and security elements incorporated in real money notes, The method retrieves important visual properties, including watermarks, security threads, holograms, and other security aspects, by using sophisticated image processing algorithms. After that, a machine-learning model for categorization is trained using these features. The suggested solution incorporates a broad dataset of real and fake banknotes. It uses a diverse dataset of authentic and counterfeit notes to train a machine-learning model. The system's accuracy and efficiency are evaluated against real-world counterfeit samples, resulting in a high detection rate and a low false-positive rate. This technology enhances financial system security and integrity. The system's effectiveness, reliability, and adaptability make it a crucial tool in combating counterfeit currency, safeguarding economic stability, and promoting trust in financial transactions