Performance Comparison of Deep Learning Techniques in Naira Classification
This work addresses automated cash handling and assistive technology for the visually impaired in Nigeria, but it is incremental as it applies existing deep learning methods to a new dataset.
This study tackled the problem of classifying Nigerian Naira currency notes by denomination using deep learning models, achieving a validation accuracy of 87.04% with MobileNetV2 on a dataset of 1,808 images.
The Naira is Nigeria's official currency in daily transactions. This study presents the deployment and evaluation of Deep Learning (DL) models to classify Currency Notes (Naira) by denomination. Using a diverse dataset of 1,808 images of Naira notes captured under different conditions, trained the models employing different architectures and got the highest accuracy with MobileNetV2, the model achieved a high accuracy rate of in training of 90.75% and validation accuracy of 87.04% in classification tasks and demonstrated substantial performance across various scenarios. This model holds significant potential for practical applications, including automated cash handling systems, sorting systems, and assistive technology for the visually impaired. The results demonstrate how the model could boost the Nigerian economy's security and efficiency of financial transactions.