mlsanityData to 14 Oct 2026

Machine Vision Using Cellphone Camera: A Comparison of deep networks for classifying three challenging denominations of Indian Coins

arXiv 2306.06084 ↗First posted 12 May 2023cs.CV cs.LGcited by 0

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Abstract (arXiv)

Indian currency coins come in a variety of denominations. Off all the varieties Rs.1, RS.2, and Rs.5 have similar diameters. Majority of the coin styles in market circulation for denominations of Rs.1 and Rs.2 coins are nearly the same except for 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 the correct denomination from either side of a coin with an accuracy of 97%.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 17 references that are in the map. First posted on arXiv 12 May 2023.

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References · 4

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  4. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795

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