Recognizing Egyptian currency for people with visual impairment using deep learning models

Abstract

Abstract This study presents a novel real-time Egyptian currency recognition system designed to assist visually impaired individuals in performing financial transactions independently and securely. The system leverages advanced deep learning models—YOLOv8, YOLOv9, and YOLOv10 to achieve high accuracy and low latency in identifying Egyptian banknotes. Evaluated on a comprehensive dataset of 2,000 annotated images, the models incorporate innovations such as context aggregation, GELAN, and NMS-free training to enhance performance. A review of prior systems highlights their limitations, especially concerning regional currencies. YOLOv10 achieved the best performance, with a precision of 0.9678, F1 score of 0.9715, and mAP@0.5 of 0.9934, surpassing both YOLOv8 and YOLOv9. Compared to traditional techniques, this approach offers significant improvements in accuracy and processing speed, providing a scalable and practical solution for accessible AI applications. These contributions promote financial independence and inclusion for visually impaired users, supporting ongoing advances in assistive technology.