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. T o enable real - time and offline functionality, the model was converted in to T ensorFlow 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 assist ive technologies. INTRODUCTION Currency plays a crucial role in financial transactions, and an efficient recognition system is essential for ensuring seamless economic operations.

Earlier this year, Microsoft released a new app called Seeing AI that changes the lives of blind and low vision community. This app uses the power of AI to describe nearby people, text and objects. Blind and low vision community people can just hold up their phone and hear information about the world around them. Yesterday, Microsoft announced Seeing AI v2.0 app with several new features and expanded availability. First of all, Seeing AI app is now available in 35 countries, including the European Union.