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

Résumé

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.

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