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Recognizing Egyptian currency for people with visual impairment using deep learning models
Ahmed M Ghanem1,2, Hassan A Youness3, Mohamed Wahba4
1Department of Computers & Systems Engineering, Faculty of Engineering, Minia University, Minya, 61519, Egypt. ahmed.addo.pg@eng.s-mu.edu.eg.
Scientific Reports
|October 1, 2025
Summary
This study introduces a real-time Egyptian currency recognition system using advanced AI models to help visually impaired individuals manage money. YOLOv10 demonstrated superior performance, enhancing financial independence and accessibility.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Assistive Technology
Background:
- Visually impaired individuals face challenges in independent financial transactions.
- Existing currency recognition systems often lack accuracy for regional currencies.
- There is a need for efficient and reliable assistive technology for financial inclusion.
Purpose of the Study:
- To develop and evaluate a real-time Egyptian currency recognition system.
- To enhance the financial independence and security of visually impaired users.
- To compare the performance of YOLOv8, YOLOv9, and YOLOv10 for banknote identification.
Main Methods:
- Utilized deep learning models: YOLOv8, YOLOv9, and YOLOv10.
- Trained and evaluated models on a dataset of 2,000 annotated Egyptian banknote images.
- Incorporated innovations like context aggregation, GELAN, and NMS-free training.
Main Results:
- YOLOv10 achieved the highest performance metrics: 0.9678 precision, 0.9715 F1 score, and 0.9934 mAP@0.5.
- The developed system demonstrated high accuracy and low latency in identifying Egyptian banknotes.
- Performance surpassed both YOLOv8 and YOLOv9, as well as traditional methods.
Conclusions:
- The novel AI system significantly improves Egyptian currency recognition for visually impaired users.
- YOLOv10 offers a scalable and practical solution for accessible AI applications.
- The system promotes financial inclusion and supports advancements in assistive technology.
