Published on: August 2026
INDIAN CURRENCY NOTE RECOGNITION FOR VISUALLY IMPAIRED WITH AUDIO FEEDBACK
Supriya V Sanjana K S Neha N Pallavi M Venu M
Article Status
Available Documents
Abstract
The system is built to automatically identify Indian currency notes using digital image processing and computer vision techniques. It takes images either from a live camera or stored files and processes them using a digital image process and CNN. The model learns important visual patterns from the notes and classifies them into their correct denominations, even when the images are not perfect due to factors like poor lighting, blur, or background disturbances.
Once the currency is identified, the detected value is transformed into voice output through a Text-to-Speech (TTS) module, allowing users to hear the detected value instantly. The entire system is optimized to be lightweight so that it can run efficiently on smartphones and embedded devices. This enables practically to use in real world. Overall, the proposed approach helps improve accessibility and supports visually impaired individuals in performing financial tasks more independently.
How to Cite this Paper
V, S., S, S. K., N, N., M, P. & M, V. (2026). Indian Currency Note Recognition for Visually Impaired with Audio Feedback. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(8), 1-9. https://doi.org/10.55041/ijcope.v2i8.069
V, Supriya, et al.. "Indian Currency Note Recognition for Visually Impaired with Audio Feedback." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 8, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijcope.v2i8.069.
V, Supriya,Sanjana S,Neha N,Pallavi M, and Venu M. "Indian Currency Note Recognition for Visually Impaired with Audio Feedback." International Journal of Creative and Open Research in Engineering and Management 02, no. 8 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijcope.v2i8.069.
Search & Index
References
- Soam, V.Tyagi, H.Rastogi, A.Jaishwal, and A.Gupta, “Cash Eye – Currency Detection for Blind People Using Machine Learning”, International Journal of Computer Techniques, vol. 13, no. 2, pp. 658–662, 2026.
- A. Shreya, M. D. A. I. Siddique, and S. Tasnim, “Real-Time Currency Detection and Voice Feedback for Visually Impaired Individuals,” arXiv preprint arXiv:2510.20267, Oct. 2025.
- L. Mathew, A. C. Vinoy, R. H. A., B.
Moozhippurath, A. D. C., and D. Korikkara, “Blind Assist System for Currency Recognition Using Sequential CNN,” in Proc. IEEE ICTEST, 2025, doi:10.1109/ICTEST64710.2025.11042604.
- Maganti, J. Gutla, Y. Mallampalli, and Y. Sandeep, “Innovative Currency Identifier for the Blind using Deep Learning,” in Proc. AUTOCOM, 2024.
- Khalid, H. B. Ul Haq, Z. Janjua, H. M. M. Akhtar, and H. M. Yousaf, “YOLOv9-Based Smart Glasses for Real-Time Recognition of Currency Notes,” Journal of Computing & Biomedical Informatics, vol. 8, no. 1, pp. 1–12, 2024.
- S. Panda, D. T. Devi, N. Asritha, G. S. Harish, and G. A. S. Varma, “Enabling Visual Independence: Image Processing for Currency Recognition in the Visually Impaired Community,” Journal of Nonlinear Analysis and Optimization, vol. 15, no. 1, pp. 1961–1966, 2024.
- Adline Macriga et al., “Currency Detector for Visually Impaired,” in Proc. ICCEBS, 2023.
- C. S. Abilash et al., “Currency Recognition for the Visually Impaired People,” IEEE, 2022.
- Saini et al., “Indian Currency Recognition: A Lightweight Transfer Learning Approach for Visually Impaired”, in Proc. PDGC, 2022.
- V. Tresa Sangeetha et al., “Currency Detection App for Blind People Using MIT App Inventor,” in Proc. ICPECTS, 2022.
Ethical Compliance & Review Process
- •All submissions are screened under plagiarism detection.
- •Review follows editorial policy.
- •Authors retain copyright.
- •Peer Review Type: Double-Blind Peer Review
- •Published on: Aug 08 2026
This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.