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Automatic detection and recognition of banknotes can be a very usefultechnology for people with visual difficulties and also for the banks itself byproviding efficient management for handling different paper currencies.Lightweight models can easily be integrated into any handy IoT basedgadgets/devices. This article presents our experiments on severalstate-of-the-art deep learning methods based on Lightweight ConvolutionalNeural Network architectures combining with transfer learning. ResNet152v2,MobileNet, and NASNetMobile were used as the base models with two differentdatasets containing Bangladeshi banknote images. The Bangla Currency datasethas 8000 Bangladeshi banknote images where the Bangla Money dataset consists of1970 images. The performances of the models were measured using both thedatasets and the combination of the two datasets. In order to achieve maximumefficiency, we used various augmentations, hyperparameter tuning, andoptimizations techniques. We have achieved maximum test accuracy of 98.88\% on8000 images dataset using MobileNet, 100\% on the 1970 images dataset usingNASNetMobile, and 97.77\% on the combined dataset (9970 images) usingMobileNet.

Deep Learning Approach Combining Lightweight CNN Architecture with  Transfer Learning: An Automatic Approach for the Detection and Recognition of  Bangladeshi Banknotes