Currency Detection for Blind using DL and AI

Visually impaired individuals face significant challenges in performing daily financial transactions due to their inability to visually recognize and differentiate between various currency denominations. This often leads to dependency on others, increased chances of fraud, and reduced independence. To address this issue, this project proposes an AI-based Currency Detection System designed specifically for blind and visually impaired users. The system utilizes Deep Learning (DL) and Computer Vision techniques to automatically identify and classify different denominations of currency in real time. The proposed system captures images of currency notes using a mobile or embedded camera. These images are then processed using advanced deep learning model MobileNet trained on a large dataset of various currency denominations under different lighting and background conditions. The model is capable of recognizing features such as color patterns, printed symbols, and texture variations unique to each denomination. Once the note is identified, the result is immediately communicated to the user, making the system completely accessible to the visually impaired. The implementation combines AI-based object detection with assistive technology, ensuring accuracy, speed, and user-friendliness. It also supports multiple currencies, making it adaptable for international use. Furthermore, the system can operate offline once trained, which enhances its practicality for real-world scenarios where internet connectivity may be limited. This project aims to promote financial independence, social inclusion, and accessibility for visually impaired individuals by harnessing the power of Artificial Intelligence and Deep Learning. The integration of image recognition, deep neural networks, and speech output provides an efficient, affordable, and real-time currency identification solution.