BD Currency Detection: A CNN Based Approach with Mobile App Integration

• URL: http://arxiv.org/abs/2502.17907v1
• Date: Tue, 25 Feb 2025 07:13:43 GMT
• Title: BD Currency Detection: A CNN Based Approach with Mobile App Integration
• Authors: Syed Jubayer Jaman, Md. Zahurul Haque, Md Robiul Islam, Usama Abdun Noor,
• Abstract summary: This study introduces an advanced currency recognition system utilizing Convolutional Neural Networks (CNNs)<n>A dataset comprising 50,334 images was collected, preprocessed, and used to train a CNN model optimized for high performance classification.<n>The trained model achieved an accuracy of 98.5%, surpassing conventional based currency recognition approaches.
• Score: 1.2535250082638645
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Currency recognition plays a vital role in banking, commerce, and assistive technology for visually impaired individuals. Traditional methods, such as manual verification and optical scanning, often suffer from limitations in accuracy and efficiency. This study introduces an advanced currency recognition system utilizing Convolutional Neural Networks (CNNs) to accurately classify Bangladeshi banknotes. A dataset comprising 50,334 images was collected, preprocessed, and used to train a CNN model optimized for high performance classification. The trained model achieved an accuracy of 98.5%, surpassing conventional image based currency recognition approaches. To enable real time and offline functionality, the model was converted into TensorFlow Lite format and integrated into an Android mobile application. The results highlight the effectiveness of deep learning in currency recognition, providing a fast, secure, and accessible solution that enhances financial transactions and assistive technologies.

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