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

arXiv: https://arxiv.org/abs/2502.17907 · HTML: https://mlsanity.com/paper/2502.17907/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 22 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Builds on (most cited first, 1 of 1)

- [Deep Learning Approach Combining Lightweight CNN Architecture with Transfer Learning: An Automatic Approach for the Detection and Recognition of Bangladeshi Banknotes](https://mlsanity.com/paper/2101.05081/) (2020)

## Built on by (most cited first, 1 of 1)

- [Quantitative Currency Evaluation in Low-Resource Settings through Pattern Analysis to Assist Visually Impaired Users](https://mlsanity.com/paper/2509.06331/) (2025)

## Abstract (arXiv)

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.