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Real-Time Recognition and Detection of Iraqi Currency Using DNN

Author

Listed:
  • Laith F. Jumma

    (Medical Instrumentation Techniques, Al-Esraa University College, Baghdad, Iraq.)

Abstract

Our daily lives are not possible without money. However, the most crucial issue at this time is how to distinguish between real and fake currencies. The accuracy of cash recognition will be dramatically increased if a computer is used, and the workload of the workforce will be much decreased. It generally uses deep neural networks to learn a dataset. This paper endeavor can make use of a wide variety of models. Accuracy of currency recognition can be increased using these models. Convolutional Neural Networks (CNN) are often quite suitable for our needs regarding money detection. The denomination and front/back sides of a piece of currency can still be determined even when it is tilted or shifted. In order to more precisely identify the denomination of the paper cash, both on the front and back, we primarily employ the CNN model in this research to extract the properties of paper currency. The primary benefits of employing CNN are the up to 98% average accuracy of currency recognition.

Suggested Citation

  • Laith F. Jumma, 2023. "Real-Time Recognition and Detection of Iraqi Currency Using DNN," Journal of Scientific Reports, IJSAB International, vol. 5(1), pages 1-7.
    • Handle: RePEc:aif:report:v:5:y:2023:i:1:p:1-7
    as

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