Year: 2025
Type: article
Abstract: Currency recognition, or digitization, refers to the process of converting physical currency, such as banknotes and coins, into digital formats. This transformation enhances convenience, security, accessibility, and cost efficiency in financial transactions, which are essential in the modern economy. This paper introduces a Convolutional Neural Network (CNN) model designed for the recognition of Bhutanese paper currency. The model was trained on a dataset comprising various currency types and denominations, achieving a training accuracy of 91 percent and a testing accuracy of 80.5 percent. The architecture consists of three convolutional layers, followed by a dense layer for classification. The findings suggest that CNNs are effective for currency recognition, with the potential for improved accuracy through the expansion of the training dataset.
Source: Zorig Melong.
DOI: 10.17102/zmv8.i1.009
Authors: Parshu Ram Dhungyel, Manoj Chhetri
Institutions: College of Science and Technology, Royal University of Bhutan
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4411437776