Currency is used almost everywhere to facilitate business.
Most developing countries in the world especially in Africa a tangible note is used predominantly.
Globally, the visually impaired population is estimated to be 285 million, of whom 39 million are vision loss (W.H.O.).
Business transactions by the blind have been extremely difficult. For instance, In shopping center, super markets, bus stations, every where.
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PREVIOUS WORKS
There have been few researches focusing on Counterfeits detection.
There haven’t been works that provide a solution to the visually impaired people in identifying currencies Especially in Africa.
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PROPOSED SOLUTION
An android and IOS compatible mobile application that recognizes the Banknote different countries.
The application has a voice integrated with it. It provides a sound when it is opened and tells the currency hold in hand in Amharic, a working language of Ethiopia.
The application is currently developed for the newly released Ethiopian Banknote (5,10,50,100,200).
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METHODOLOGY
A Convolutional Neural Network (CNN) is used to develop the model. And TensorFlow lite is used to support a mobile application.
A total dataset of 2060 images are used for developing and testing including the Unknown class.
The model achieved 98.9% accuracy.
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CONCLUSION
Currently, we are working with the Ethiopian National Association of the Blind (ENAB) to test the usability of the application.
Widespread adoption of this technology will greatly enhance the ability of the visually impaired to transact business independently.
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FUTURE WORKS
We are working to integrate more languages of Ethiopia to the application and reach more people.
Work on other African countries to support more visually impaired people.
Support for more objects or tools that are routinely used by the visually impaired.