Fiscal classification using convolutional neural network
Namita Bindal, Rajat Modi, Brajesh Kumar Kaushik · 2020
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.