Abstract
A system for the recognition of currency notes is one kind of intelligent system which is a very important need for visually impaired and blind users in the modern world of today. In this paper, we present a currency recognition app applied to Indian currency notes. Our proposed system is based on interesting features and correlations between images. It uses the Convolutional Neural Network for classification. The method takes Indian rupee paper currencies as a model. The method is quite reasonable in terms of accuracy. The system deals with the images of all the currency note denominations, some of which are tilted to an angle less than 150. The rest of the currency images consist of mixed, noisy, and normal images. It uses the current series (1996–2020) of currency issued by the Reserve Bank of India (RBI) as a model currency under consideration. The system produces an accuracy of recognition of 94.38% and gives an audio output to the users. The proposed technique produces quite satisfactory results in terms of recognition and efficiency. In the future, this app can be improved by adding a dataset of other currency notes of the world.
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Bhutkar, G., Patil, M., Patil, D., Mukunde, S., Shinde, R., Rathod, A. (2022). Currency Recognition App for Visually Impaired Users in India. In: Bhutkar, G., et al. Human Work Interaction Design. Artificial Intelligence and Designing for a Positive Work Experience in a Low Desire Society. HWID 2021. IFIP Advances in Information and Communication Technology, vol 609. Springer, Cham. https://doi.org/10.1007/978-3-031-02904-2_10
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DOI: https://doi.org/10.1007/978-3-031-02904-2_10
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