Currency Identification with Real-Time Value Conversion

DOI

10.2991/978-94-6463-858-5_241How to use a DOI?

Keywords

Currency Recognition; Digital Image Processing; Convolutional Neural Networks (CNN); Edge Detection; Feature Extraction; Machine Learning

Abstract

In this article, we present an automatic currency recognition system based on digital image processing methods. The system is designed to identify currency notes through major features such as size, color, and printed text such that users are able to identify major details such as currency value, denomination, and exchange rates in INR, EURO, and USD. The proposed system primarily focuses on Indian Rupees (INR) and US Dollars (USD), which are two most used currencies. The system employs edge detection and Convolutional Neural Networks (CNN) for feature selection, extraction, and classification of currency bills. Image processing is used to sharpen the input image prior to comparing it with data stored. Additionally, an online exchange rate API is incorporated to retrieve real-time currency conversion rates. This strategy provides precise and effective recognition, making the system very appropriate for travelers, banks, and automatic vending machines.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - K. Archana
AU  - K. Chandrika
AU  - M. Ajay
AU  - P. Phannendra
PY  - 2025
DA  - 2025/11/04
TI  - Currency Identification with Real-Time Value Conversion
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 2869
EP  - 2880
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_241
DO  - 10.2991/978-94-6463-858-5_241
ID  - Archana2025
ER  -