A Model for Recognition and Detection of the Counterfeit of Ethiopian Banknotes using Transfer Learning
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Date
2024-06
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Addis Ababa University
Abstract
Paper currency recognition systems play a pivotal role in various sectors, including banking, retail, and automated teller machines (ATMs). This paper presents a novel approach to the design and development of a paper currency recognition system using customized deep learning techniques. The proposed system utilizes image-processing algorithms to extract features from currency images, followed by customized convolutional neural network models for classification and detection of the counterfeit. The system is trained on a diverse dataset of currency images to ensure robustness and accuracy in recognizing various denominations and currencies. We implemented feature learning techniques architectures. To obtain the best accuracy and efficiency we used RLUs and Softmax as an activation, Adam optimizer, and sparse categorical cross-entropy as a loss function for both as a training strategy. The data was collected from the National Bank of Ethiopia, Commercial Bank of Ethiopia, NIB International Bank, and Bank of Abyssinia. From the experimental results of the alex_customed-design network, 99.82% accuracy is recorded.
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Paper Currency, Deep Learning, Machine Learning, Artificial Intelligence, Alexnet