Machine Vision Using Cellphone Camera: A comparison of deep networks for classifying three challenging denominations of Indian coins
Keyur D. Joshi, Dhruv Shah, Varshil Shah, Nilay Gandhi, Sanket J. Shah, Sanket B. Shah · 2022
Indian currency coins come in a variety of denominations. Off all the varieties$\unicode{x20B9}1,\unicode{x20B9}2$, and$\unicode{x20B9}5$have similar diameters. Majority of the coin styles in market circulation for denominations of$\unicode{x20B9}1$and$\unicode{x20B9}2$coins are nearly the same except numerals on its reverse side. If a coin is resting on its obverse side, the correct denomination is not distinguishable by humans. Therefore, it was hypothesized that a digital image of a coin resting on its either size could be classified into its correct denomination by training a deep neural network model. The digital images were generated by using cheap cell phone cameras. To find the most suitable deep neural network architecture, four were selected based on the preliminary analysis carried out for comparison. The results confirm that two of the four deep neural network models can classify correct denomination from either side of a coin with accuracy of 97%.