Currency Detection App for the Visually Impaired

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dc.contributor.authorDANIEL MARK FERNANDES
dc.contributor.authorJOSHUA DAVID CRASTO
dc.contributor.authorMCKENZIE CHRISTO PEREIRA
dc.contributor.authorMILROY ADRIEL FERNANDES
dc.contributor.authorSEAN ROSARIO D’SILVA
dc.date.accessioned2026-05-25T07:05:27Z
dc.date.available2026-05-25T07:05:27Z
dc.date.issued2026
dc.description.abstractThe increased reliance on digital technologies has also led to the development of many new opportunities for creating technologies that can improve accessibility for the visually impaired. In this project, the development of a mobile application that helps users recognize currency notes and coins through real-time image processing has been presented. This application utilizes the YOLO algorithm for accurate recognition of the currency, ensuring the efficient execution of the model with the help of TensorFlow Lite. This application, developed with React, has been designed with accessibility as a primary feature. This application has been integrated with features such as TalkBack, allowing users to navigate the application with the help of gestures, providing users with accurate and timely feedback through the audio output. This application has been designed with the aim of promoting accessibility for the visually impaired. In addition to the technical aspect, the project also focuses on usability, reliability, and practical application. It should be noted that the system operates efficiently on current Android-based gadgets. It provides real-time operation without the need to maintain a constant internet connection. Testing was conducted to ensure the accuracy of the detection process under normal usage. Through the integration of intelligent detection systems and usability principles, the application provides a reliable and accessible tool that plays a vital role in the development of assistive technologies.
dc.identifier.urihttp://rcca.ndl.gov.in/handle/123456789/621
dc.language.isoen
dc.publisherASST. PROF. ANVI NAIK
dc.titleCurrency Detection App for the Visually Impaired
dc.typeOther
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Bachelor of Computer applications