Abstract
This paper offers a comparative analysis of various deep learning models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Deep Belief Networks (DBNs), applied to the task of banknote recognition. Accurate banknote recognition is indispensable in financial transactions, vending machines, and security systems, and the development of reliable recognition systems is essential to mitigate fraud and ensure seamless operation. In this study, we employed the Banknote dataset, containing a balanced mixture of genuine and forged banknotes, to train and test these models. Our ex-perimental results demonstrate that the CNN model, specifically with ResNet-50 architecture, outperforms the others in terms of accuracy, precision, recall, and F1 score. The paper encapsulates the performance metrics and considerations for applying these models to banknote recognition, and aims to assist researchers and practitioners in choosing the most effective methods for future development of banknote recognition systems.