ItemArticle

Improved Indian currency recognition: neighbourhood-centred image processing and CNNs with region of pixel selection techniques

Title
Improved Indian currency recognition: neighbourhood-centred image processing and CNNs with region of pixel selection techniques
Creator
Mopuru, Bhargavi; Sinha, Anurag; Agrawal, Ankita; Nandan, Amritesh; Agarwal, Ankit; Nikitha, Peddi; Sharma, Vandana; Anand, Ankit; Keskin, Do?an; Kumar, Biresh; Jha, Pooja
Description
The paper proposes an improved approach for Indian currency recognition using neighbourhood-centred image processing and convolutional neural networks (CNNs) with region of pixel selection techniques. The method includes image pre-processing steps such as noise reduction, contrast enhancement, and resizing. A neighbourhood-centred image processing technique is applied to capture contextual information from local neighbourhoods around each pixel. A CNN-based model is then trained on the pre-processed images to learn discriminative features for currency recognition. To enhance accuracy and efficiency, a region of pixel selection technique is introduced to select only relevant regions of interest for CNN training and inference, reducing computational overhead. Experimental results demonstrate the effectiveness of the proposed approach, achieving high accuracy in currency recognition and improved efficiency in terms of computational time and memory requirements. The proposed method has potential applications in automated cash-handling machines, vending machines, and mobile payment systems where reliable currency recognition is essential. Copyright 2025 Inderscience Enterprises Ltd.
Source
International Journal of Services, Economics and Management;Volume;16;Issue;2026-05-04 00:00:00;pp.482-516
Date
01-01-2025
Publisher
Inderscience Publishers
Subject
CNN; currency recognition; decision boundary; features; image processing; image processing; RGB; threshold
Coverage
Mopuru B., Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Andhra Pradesh, 520002, India; Sinha A., Department of Computer Science, IGNOU, India; Agrawal A., Department of Commerce, Indian Institute of Management Kozhikode Kerala, Kozhikode, India; Nandan A., Department of Computer Science, Graphic Era (Deemed to be University), Uttarakhand, 248002, India; Agarwal A., Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu, Virudhunagar, India; Nikitha P., Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu, Virudhunagar, India; Sharma V., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, India; Anand A., Department of Computer Science, Amity University, Jharkhand, India; Keskin D., Department of Computer Science, Turkish German University, Istanbul, Turkey; Kumar B., Department of Information Technology, Amity University Jharkhand, Ranchi, India; Jha P., Department of Information Technology, Amity University Jharkhand, Ranchi, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 17530822;
Format
online
Language
English
Type
Article

Citation

Mopuru, Bhargavi; Sinha, Anurag; Agrawal, Ankita; Nandan, Amritesh; Agarwal, Ankit; Nikitha, Peddi; Sharma, Vandana; Anand, Ankit; Keskin, Do?an; Kumar, Biresh; Jha, Pooja, “Improved Indian currency recognition: neighbourhood-centred image processing and CNNs with region of pixel selection techniques,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/23317.

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