Fiscal classification using convolutional neural network

Namita Bindal; Rajat Modi; Brajesh Kumar Kaushik

doi:10.1117/12.2568314

20 August 2020 Fiscal classification using convolutional neural network

Namita Bindal, Rajat Modi, Brajesh Kumar Kaushik

Proceedings Volume 11469, Emerging Topics in Artificial Intelligence 2020; 114691N (2020) https://doi.org/10.1117/12.2568314
Event: SPIE Nanoscience + Engineering, 2020, Online Only

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Abstract

Currency classification is an important task in computer vision. Traditional models extract relevant features (brightness, shape, colour etc.) through complex mathematical calculations. A deep learning approach towards fiscal classification by automatically learning higher order feature representations of the dataset is presented. A family of Resnet models is trained to minimize the effect of distortions in currency dataset. The classifier achieves a peak test set performance of 98.09% and an ensembled accuracy of 98.3%. Finally, an optimization method is introduced to allow the models initialized with pretrained weights to converge faster and achieve better accuracy in certain cases.

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Namita Bindal, Rajat Modi, and Brajesh Kumar Kaushik "Fiscal classification using convolutional neural network", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 114691N (20 August 2020); https://doi.org/10.1117/12.2568314

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