Jordanian banknote data recognition: A CNN-based approach with attention mechanism

Abstract

Identifying counterfeit banknotes is crucial in financial transactions, as the process of identification cannot be handled by ATMs or vending machines. The recent developments in technology, particularly the smart systems that are integrated with cameras and artificial intelligence (AI) tools, allow for the distinction of currency and the detection of counterfeit. In this study, we are suggesting an approach for identifying counterfeit Jordanian banknotes and differentiating them from genuine ones. The suggested approach collaborates deep learning through a convolutional neural network (CNN) and another attention mechanism which contributes to focusing on features of importance while avoiding features of less importance. The proposed model has proven its ability to recognize counterfeits with high performance and accuracy while focusing on the important features extracted. The study made use of a data set from Kaggle that includes a collection of Jordanian banknotes in five different denominations. Image processing techniques were employed to produce artificial images by boosting the brightness of real ones. Eight trained models and the suggested model were compared. It demonstrated excellence with encouraging outcomes, achieving 96% accuracy, 96.6% precision, 96.4% recall, and 94.5% f1-score. Also, we tested our approach on two datasets Indian dataset and DS1, DS2, and DS3 datasets, we obtained 88% and 99.9% accuracy, respectively. The achievement of detecting counterfeit Jordanian banknotes is proof that a well-established AI model contributes to dealing with security vulnerabilities in many institutions.

Keywords

• Convolution neural networks
• Attention mechanism
• Banknotes classification
• Deep learning