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Review Article
Contribution of Machine and Deep Learning methodologies in the identification of counterfeit currency notes
Dileep Kumar R1
Hari Mohan Singh2
Anchit Sajal Dhar3
Ashhad Imam4
1 2 3 Department of Computer Science& IT, Sam Higginbottom University of Agriculture Technology & Sciences, Prayagraj, Uttar Pradesh, India. 4 Department of Civil Engineering, Sam Higginbottom University of Agriculture Technology & Sciences, Prayagraj, Uttar Pradesh, India.
Published Online: March-April 2026
Pages: 01-11
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260702001
References
1. U. Sadyk et.al., “State-of-the-Art Review of Deep Learning Methods in Fake Banknote Recognition Problem”, International Journal of Advanced Computer Science and Applications, vol.15, no.1, pp. 615-624, 2024.
2. S. Kore et al., “A Survey on Fake Currency Detection”, International Journal of Research Publication and Reviews, 2023.
3. S. Suraj et al., “Survey Paper on Fake Currency Detection Using Image Processing”, International Research Journal of Modernization in Engineering Technology and Science, vol.4, issue.11, 2022.
4. M. Gour et al., “Currency and Counterfeit Currency Detection Using Machine Learning Algorithms”, Proceedings of Global Conference on Information Technologies and Communications, 2023.
5. S. C. D. Bandu et al., “Indian Fake Currency Detection Using Image Processing and Machine Learning”, Int. J. Inf. Technology, vol.16, no.8, pp. 4953-4966, 2024.
6. N. G. Abadi et al., “Machine Learning-Based Authentication of Banknotes”, Int. J. Electr. Comput. Eng., 2024.
7. Konne Priyanka et.al. “An Enhanced Machine Learning Approach for Currency Authentication using Multi-Region Feature Analysis and Random Forest Classification”, International Journal of Engineering Research & Technology, Vol:14, Issue:07, 2025, DOI : 10.17577/IJERTV14IS070035
8. Nama’a M.Z. Hamed et. al., “Advanced Methods for Identifying Counterfeit Currency: Using Deep Learning and Machine Learning”, NTU Journal of Engineering & Technology, vol.3, no.3 pages:36-45, 2024.
9. R. S. Khairy et al., “The Detection of Counterfeit Banknotes Using Ensemble Learning Algorithms”, Int. J. Intell. Eng. Syst., vol. 14, no. 2, 2021.
10. Navaneetha K R et. al., "Real-Time Fake Currency Detection Using CNN", International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2022.116116
11. C.-Y. Yeh, W.-P. Su, and S.-J. Lee, “Employing Multiple-Kernel Support Vector Machines for Counterfeit Banknote Recognition”, Appl. Soft Comput., vol. 11, pp. 1439–1447, 2011.
12. L. Wang, “Automated Detection and Classification of Counterfeit Banknotes Using Spectral-Domain Optical Coherence Tomography”, Sci. Justice, vol. 62, no. 5, 2022.
13. Jegnaw Fentahun Zeggeye et.al., “ Automatic Recognition and Counterfeit Detection of Ethiopian Paper Currency”, I.J. Image, Graphics and Signal Processing, 2016, 2, 28-36, DOI: 10.5815/ijigsp.2016.02.04
14. Young Ho Park et. al, “A High Performance Banknote Recognition System Based on a One-Dimensional Visible Light Line Sensor”, sensors, vol.15, pp.14093-14115; doi:10.3390/s150614093, 2015.
15. Srinivas Arukonda et. al., “WebAuthML: A Web-Based Approach for Banknote Authentication Using Machine Learning and Image Processing”, Proceedings. IEEE- INDICON, 2024.
16. E. Choi et al., “Machine Learning-Based Fast Banknote Serial Number Recognition”, J. Inf. Process. Syst., vol. 15, no. 4, 2019.
17. R K Yadav et.al, “Counterfeit Currency Detection Using Machine Learning”, Machine Learning for Predictive Analysis: Proceeding of ICTIS, Springer 2020.
18. Santosh Gopane et.al., “ Indian Counterfeit Banknote Detection Using Support Vector Machine”, SSRN Electronic Journal · January 2020 DOI: 10.2139/ssrn.3568724.
19. Aman Bhatia et. al., “Fake Currency Detection with Machine Learning Algorithm and Image Processing”, 5th International Conference on Intelligent Computing and Control Systems (ICICCS), IEEE Xplore DOI : 10.1109/ICICCS51141.2021.9432274
20. C. G. Pachón, D. M. Ballesteros, and D. Renza, “Fake Banknote Recognition Using Deep Learning”, Applied Sciences., vol. 11, no. 3, p. 1281, 2021.
21. D. Tong, “Visual Watermark Identification from the Transparent Window on Polymer Banknotes”, in Visual Information Processing for Security Applications, IGI Global, 2021.
22. A. Nasayreh et al., “Jordanian Banknote Data Recognition: A CNN-Based Approach with Attention Mechanism”, J. King Saud Univ. Comput. Inf. Sci., vol.36, no.4, 2024.
23. T. D. Pham et al., “Deep Learning-Based Fake-Banknote Detection for the Visually Impaired People Using Visible- Light Images Captured by Smartphone Cameras”, IEEE Access, vol.8, pp. 63144-63161, 2020.
24. Karn Na Sritha et al., “Counterfeit Thai Banknote Detection Using Deep Learning”, Proceedings: International Conference on Autonomous Systems, pp. 1-6, 2025.
25. R. Abbas et al., “Fake Currency Detection Using Convolutional Neural Network and OCR-Based Text Validation”, Proceedings: International Conference on Intelligent Systems & Digital Transformation, 2025.
26. P. Chhabra et.al., “A Two-Stage Deep Learning-Based Framework for Counterfeit Detection of Indian Banknotes Using YOLO-NAS and UV Imaging”, Evergreen, vol. 12, no. 3, 2025.
27. C. G. Pachón et. al., “An Efficient Deep Learning Model Using Network Pruning for Fake Banknote Recognition”, Expert Systems Applicaions., vol. 233, 120961, 2023.
28. Abhijit Pathak et. al., “ Enhanced Counterfeit Detection of Bangladesh Currency through Convolutional Neural Networks: A Deep Learning Approach”, International Journal of Innovative Research in Computer Science and Technology , ISSN(Online): 2347-5552, Vol.12, Issue 2, 2024, https://doi.org/10.55524/ijircst.2024.12.2.1.
29. S. J. Jaman et al., “BD Currency Detection: A CNN-Based Approach with Mobile App Integration”,arXiv:2502.17907, 2025.
30. K. Rana et.al., “Deep Learning-Based Fake Banknote Detection System”, Journal of Emerging Technology and Innovative Research, vol. 12, no. 4, 2025.
31. D. Jahnavi et. al., “Fake Currency Detection Using Convolutional Neural Networks”, Journal of Electronic Design Technology, vol.14, No.2, 2023.
32. M. Han et al., “Joint Banknote Recognition and Counterfeit Detection Using Convolutional Neural Networks”, Sensors, vol. 19, no. 16, 3607, 2019.
33. Ruheena et. al., “Fake Currency Detection Using Deep Learning Technique”, International Journal of Engineering Research and Technology, vol. 10, issue 12, 2022.
34. Kriti Rana et. al, “Deep Learning-Based Fake Banknote Detection System”, Journal of Emerging Technologies and Innovative Research (JETIR) vol. 12, no. 4, 2025.
35. T. D. Pham et al., “Multi-National Banknote Classification Based on Visible-Light Banknote Images”, Sensors, vol. 17, no. 7, 2017.
36. Oladapo Ibitoye, et. al., “Fake Currency Detection using Modified Faster Region-Based Convolutional Neural Network”, International Journal of Electrical Engineering and Computer Science, 2024, DOI: 10.37394/232027.2024.6.5
37. A.S.K.S.K. Reddy et.al, “Detection of Fake Banknote Using Deep Learning (YOLOv3 and CNN)”, Int. Res. J. Mod. Eng. Eng. Technol. Sci.(IRJMETS), vol.6, no.5, 2024.
38. Ahmad Nasayreh et.al., “Jordanian banknote data recognition: A CNN-based approach with attention mechanism”, Elsevier, B.V. on behalf of King Saud University., 2024
39. Ejaru N.P., et. al., “Counterfeit Currency Detection Leveraging MobileNet and ResNet Models”, Proceedings, EAI Conference, http://dx.doi.org/10.4108/eai.28-4-2025.2357812., 2025.
40. Ayush Antre et.al., “ Fake Currency Detection Using Convolution Neural Network “, International Research Journal of Modernization in Engineering Technology and Science, Volume:05, Issue:04, April-2023, e-ISSN: 2582-5208
41. Marjuk Ahmed Siddiki et. al., “Bangladeshi Currency Identification and Fraudulence Detection Using Deep Learning and Feature Extraction”, International Journal of Computer Science and Mobile Computing, Jan’ 2023, DOI: 10.47760/ijcsmc.2022.v12i01.001
42. Chanhum Park et.al., “Deep Feature- Based Three-Stage Detection of Banknotes and Coins for Assisting Visually Impaired People”, IEEE Access, Volume 8, 2020
43. Lavanya M. et.al.,“ Real Time Fake Currency Note Detection using Deep Learning”, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-9 Issue-1S5, December, 2019
44. Y. D. Woldehana et al., “An Explainable Counterfeit and Genuine Ethiopian Banknote Detection System”, Journal of Emerging Computer Technologies, vol. 5, Issue 1, pp 24-35, 2024.
45. Shamika Desai et.al., “CNN based Counterfeit Indian Currency Recognition Using Generative Adversarial Network”, International Conference on Artificial Intelligence and Smart Systems, IEEE Xplore Part Number: CFP21OAB-ART; ISBN: 978-1-7281-9537-7
46. Soo-Hyeon Lee1 et.al., “Counterfeit Bill Detection Algorithm using Deep Learning”, International Journal of Applied Engineering Research, ISSN 0973-4562 Volume 13, Number 1, pp. 304-310, 2018
47. Navya Krishna G et.al., “Recognition of Fake Currency Note using Convolutional Neural Networks”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) , ISSN: 2278-3075, Volume- 8, Issue-5 March, 2019.
48. D. B. Gajjar et al., “DeViTC: Deep-Vision Transformer to Recognize Originality of Currency”, IEEE Xplore, Vol 58, Issue 5, DOI:10.1109/MC.2024.3514151, 2025.
49. Raj Gaurang Tiwari et. al. “CurrencyNet: A Vision Transformer-Based Approach for Indian Currency Note Classification with Optimizer Exploration”, IEEE Xplore, DOI: 10.1109/IDCIoT59759.2024.10468015, 2024.
50. M. Tarawneh, “Hybrid Artificial Intelligence Approach to Counterfeit Currency Detection”, Int. J. Electrical and Computer Engineering, vol. 15, no. 6, 2025.
51. Franciskus Antonius et. al., “DeepCyberDetect: Hybrid AI for Counterfeit Currency Detection with GAN-CNN- RNN using African Buffalo Optimization”, International Journal of Advanced Computer Science and Applications, Vol. 14, No. 7, 2023.
52. Dulari B. Gajjar et.al., ”DeViTC_Deep Vision Transformer to Recognize Originality of Currency”, IEEE Journal, pp. 48-56, vol. 58,Issue 5, 2025
53. X. Ma and Y. Yan, “Banknote Serial Number Recognition Using Deep Learning”, Multimedia Tools Applications, Vol.80, No.13, pp. 18445-18459, 2021.
54. Mendeley Data, "Indian Currency Dataset," Mendeley Data, 2020. [Online]. Available: https://data.mendeley.com/datasets/48ympv8jjf/1. DOI: 10.17632/48ympv8jjf.1
55. PyPI Ahmad, "Indian Rupees and Thai Baht Banknotes," Kaggle. [Online]. Available: https://www.kaggle.com/datasets/pypiahmad/indian-rupees-and-thai-baht-banknotes.
56. Keyush Nisar, "In the Wild Currency Images," Kaggle. [Online]. Available:https://www.kaggle.com/datasets/keyushnisar/in-the-wild-currency-images.
57. Mendeley Data, "Dataset," Mendeley Data. [Online]. Available:https://data.mendeley.com/datasets/xn44yz596n.
58. Shuvo Kumar Basak, "Bangladeshi Currency Old/New/Coins and Notes Dataset," Kaggle. [Online]. Available: https://www.kaggle.com/datasets/shuvokumarbasak2030/bangladeshi-currency-oldnewcoins- and-notes-dataset.
59. Data in Brief, "Article," Sci. Direct. [Online]. Available:https://www.sciencedirect.com/science/article/pii/S2352340925005785.
60. Tazwar Mohammed, "Augmented Bangla Money Dataset with 200 Taka," Kaggle. [Online]. Available:https://www.kaggle.com/datasets/tazwarmohammed/augmented-bangla-money-dataset-with-200-taka .
61. Ismail Ismail Tijjani, "Naira Nigerian Currency Dataset," Kaggle. [Online]. Available: https://www.kaggle.com/datasets/ismailismailtijjani/naira-nigerian-currency-datasetscribbr.
62. Aishwarya Techie, "USD Bill Classification Dataset," Kaggle. [Online]. Available:https://www.kaggle.com/datasets/aishwaryatechie/usd-bill-classification-dataset.
63. Sajjad FC13, "Bangla Currency Detection Using CNN," Kaggle. [Online]. Available: https://www.kaggle.com/code/sajjadfc13/bangla-currency-detection-using-cnn
2. S. Kore et al., “A Survey on Fake Currency Detection”, International Journal of Research Publication and Reviews, 2023.
3. S. Suraj et al., “Survey Paper on Fake Currency Detection Using Image Processing”, International Research Journal of Modernization in Engineering Technology and Science, vol.4, issue.11, 2022.
4. M. Gour et al., “Currency and Counterfeit Currency Detection Using Machine Learning Algorithms”, Proceedings of Global Conference on Information Technologies and Communications, 2023.
5. S. C. D. Bandu et al., “Indian Fake Currency Detection Using Image Processing and Machine Learning”, Int. J. Inf. Technology, vol.16, no.8, pp. 4953-4966, 2024.
6. N. G. Abadi et al., “Machine Learning-Based Authentication of Banknotes”, Int. J. Electr. Comput. Eng., 2024.
7. Konne Priyanka et.al. “An Enhanced Machine Learning Approach for Currency Authentication using Multi-Region Feature Analysis and Random Forest Classification”, International Journal of Engineering Research & Technology, Vol:14, Issue:07, 2025, DOI : 10.17577/IJERTV14IS070035
8. Nama’a M.Z. Hamed et. al., “Advanced Methods for Identifying Counterfeit Currency: Using Deep Learning and Machine Learning”, NTU Journal of Engineering & Technology, vol.3, no.3 pages:36-45, 2024.
9. R. S. Khairy et al., “The Detection of Counterfeit Banknotes Using Ensemble Learning Algorithms”, Int. J. Intell. Eng. Syst., vol. 14, no. 2, 2021.
10. Navaneetha K R et. al., "Real-Time Fake Currency Detection Using CNN", International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2022.116116
11. C.-Y. Yeh, W.-P. Su, and S.-J. Lee, “Employing Multiple-Kernel Support Vector Machines for Counterfeit Banknote Recognition”, Appl. Soft Comput., vol. 11, pp. 1439–1447, 2011.
12. L. Wang, “Automated Detection and Classification of Counterfeit Banknotes Using Spectral-Domain Optical Coherence Tomography”, Sci. Justice, vol. 62, no. 5, 2022.
13. Jegnaw Fentahun Zeggeye et.al., “ Automatic Recognition and Counterfeit Detection of Ethiopian Paper Currency”, I.J. Image, Graphics and Signal Processing, 2016, 2, 28-36, DOI: 10.5815/ijigsp.2016.02.04
14. Young Ho Park et. al, “A High Performance Banknote Recognition System Based on a One-Dimensional Visible Light Line Sensor”, sensors, vol.15, pp.14093-14115; doi:10.3390/s150614093, 2015.
15. Srinivas Arukonda et. al., “WebAuthML: A Web-Based Approach for Banknote Authentication Using Machine Learning and Image Processing”, Proceedings. IEEE- INDICON, 2024.
16. E. Choi et al., “Machine Learning-Based Fast Banknote Serial Number Recognition”, J. Inf. Process. Syst., vol. 15, no. 4, 2019.
17. R K Yadav et.al, “Counterfeit Currency Detection Using Machine Learning”, Machine Learning for Predictive Analysis: Proceeding of ICTIS, Springer 2020.
18. Santosh Gopane et.al., “ Indian Counterfeit Banknote Detection Using Support Vector Machine”, SSRN Electronic Journal · January 2020 DOI: 10.2139/ssrn.3568724.
19. Aman Bhatia et. al., “Fake Currency Detection with Machine Learning Algorithm and Image Processing”, 5th International Conference on Intelligent Computing and Control Systems (ICICCS), IEEE Xplore DOI : 10.1109/ICICCS51141.2021.9432274
20. C. G. Pachón, D. M. Ballesteros, and D. Renza, “Fake Banknote Recognition Using Deep Learning”, Applied Sciences., vol. 11, no. 3, p. 1281, 2021.
21. D. Tong, “Visual Watermark Identification from the Transparent Window on Polymer Banknotes”, in Visual Information Processing for Security Applications, IGI Global, 2021.
22. A. Nasayreh et al., “Jordanian Banknote Data Recognition: A CNN-Based Approach with Attention Mechanism”, J. King Saud Univ. Comput. Inf. Sci., vol.36, no.4, 2024.
23. T. D. Pham et al., “Deep Learning-Based Fake-Banknote Detection for the Visually Impaired People Using Visible- Light Images Captured by Smartphone Cameras”, IEEE Access, vol.8, pp. 63144-63161, 2020.
24. Karn Na Sritha et al., “Counterfeit Thai Banknote Detection Using Deep Learning”, Proceedings: International Conference on Autonomous Systems, pp. 1-6, 2025.
25. R. Abbas et al., “Fake Currency Detection Using Convolutional Neural Network and OCR-Based Text Validation”, Proceedings: International Conference on Intelligent Systems & Digital Transformation, 2025.
26. P. Chhabra et.al., “A Two-Stage Deep Learning-Based Framework for Counterfeit Detection of Indian Banknotes Using YOLO-NAS and UV Imaging”, Evergreen, vol. 12, no. 3, 2025.
27. C. G. Pachón et. al., “An Efficient Deep Learning Model Using Network Pruning for Fake Banknote Recognition”, Expert Systems Applicaions., vol. 233, 120961, 2023.
28. Abhijit Pathak et. al., “ Enhanced Counterfeit Detection of Bangladesh Currency through Convolutional Neural Networks: A Deep Learning Approach”, International Journal of Innovative Research in Computer Science and Technology , ISSN(Online): 2347-5552, Vol.12, Issue 2, 2024, https://doi.org/10.55524/ijircst.2024.12.2.1.
29. S. J. Jaman et al., “BD Currency Detection: A CNN-Based Approach with Mobile App Integration”,arXiv:2502.17907, 2025.
30. K. Rana et.al., “Deep Learning-Based Fake Banknote Detection System”, Journal of Emerging Technology and Innovative Research, vol. 12, no. 4, 2025.
31. D. Jahnavi et. al., “Fake Currency Detection Using Convolutional Neural Networks”, Journal of Electronic Design Technology, vol.14, No.2, 2023.
32. M. Han et al., “Joint Banknote Recognition and Counterfeit Detection Using Convolutional Neural Networks”, Sensors, vol. 19, no. 16, 3607, 2019.
33. Ruheena et. al., “Fake Currency Detection Using Deep Learning Technique”, International Journal of Engineering Research and Technology, vol. 10, issue 12, 2022.
34. Kriti Rana et. al, “Deep Learning-Based Fake Banknote Detection System”, Journal of Emerging Technologies and Innovative Research (JETIR) vol. 12, no. 4, 2025.
35. T. D. Pham et al., “Multi-National Banknote Classification Based on Visible-Light Banknote Images”, Sensors, vol. 17, no. 7, 2017.
36. Oladapo Ibitoye, et. al., “Fake Currency Detection using Modified Faster Region-Based Convolutional Neural Network”, International Journal of Electrical Engineering and Computer Science, 2024, DOI: 10.37394/232027.2024.6.5
37. A.S.K.S.K. Reddy et.al, “Detection of Fake Banknote Using Deep Learning (YOLOv3 and CNN)”, Int. Res. J. Mod. Eng. Eng. Technol. Sci.(IRJMETS), vol.6, no.5, 2024.
38. Ahmad Nasayreh et.al., “Jordanian banknote data recognition: A CNN-based approach with attention mechanism”, Elsevier, B.V. on behalf of King Saud University., 2024
39. Ejaru N.P., et. al., “Counterfeit Currency Detection Leveraging MobileNet and ResNet Models”, Proceedings, EAI Conference, http://dx.doi.org/10.4108/eai.28-4-2025.2357812., 2025.
40. Ayush Antre et.al., “ Fake Currency Detection Using Convolution Neural Network “, International Research Journal of Modernization in Engineering Technology and Science, Volume:05, Issue:04, April-2023, e-ISSN: 2582-5208
41. Marjuk Ahmed Siddiki et. al., “Bangladeshi Currency Identification and Fraudulence Detection Using Deep Learning and Feature Extraction”, International Journal of Computer Science and Mobile Computing, Jan’ 2023, DOI: 10.47760/ijcsmc.2022.v12i01.001
42. Chanhum Park et.al., “Deep Feature- Based Three-Stage Detection of Banknotes and Coins for Assisting Visually Impaired People”, IEEE Access, Volume 8, 2020
43. Lavanya M. et.al.,“ Real Time Fake Currency Note Detection using Deep Learning”, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-9 Issue-1S5, December, 2019
44. Y. D. Woldehana et al., “An Explainable Counterfeit and Genuine Ethiopian Banknote Detection System”, Journal of Emerging Computer Technologies, vol. 5, Issue 1, pp 24-35, 2024.
45. Shamika Desai et.al., “CNN based Counterfeit Indian Currency Recognition Using Generative Adversarial Network”, International Conference on Artificial Intelligence and Smart Systems, IEEE Xplore Part Number: CFP21OAB-ART; ISBN: 978-1-7281-9537-7
46. Soo-Hyeon Lee1 et.al., “Counterfeit Bill Detection Algorithm using Deep Learning”, International Journal of Applied Engineering Research, ISSN 0973-4562 Volume 13, Number 1, pp. 304-310, 2018
47. Navya Krishna G et.al., “Recognition of Fake Currency Note using Convolutional Neural Networks”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) , ISSN: 2278-3075, Volume- 8, Issue-5 March, 2019.
48. D. B. Gajjar et al., “DeViTC: Deep-Vision Transformer to Recognize Originality of Currency”, IEEE Xplore, Vol 58, Issue 5, DOI:10.1109/MC.2024.3514151, 2025.
49. Raj Gaurang Tiwari et. al. “CurrencyNet: A Vision Transformer-Based Approach for Indian Currency Note Classification with Optimizer Exploration”, IEEE Xplore, DOI: 10.1109/IDCIoT59759.2024.10468015, 2024.
50. M. Tarawneh, “Hybrid Artificial Intelligence Approach to Counterfeit Currency Detection”, Int. J. Electrical and Computer Engineering, vol. 15, no. 6, 2025.
51. Franciskus Antonius et. al., “DeepCyberDetect: Hybrid AI for Counterfeit Currency Detection with GAN-CNN- RNN using African Buffalo Optimization”, International Journal of Advanced Computer Science and Applications, Vol. 14, No. 7, 2023.
52. Dulari B. Gajjar et.al., ”DeViTC_Deep Vision Transformer to Recognize Originality of Currency”, IEEE Journal, pp. 48-56, vol. 58,Issue 5, 2025
53. X. Ma and Y. Yan, “Banknote Serial Number Recognition Using Deep Learning”, Multimedia Tools Applications, Vol.80, No.13, pp. 18445-18459, 2021.
54. Mendeley Data, "Indian Currency Dataset," Mendeley Data, 2020. [Online]. Available: https://data.mendeley.com/datasets/48ympv8jjf/1. DOI: 10.17632/48ympv8jjf.1
55. PyPI Ahmad, "Indian Rupees and Thai Baht Banknotes," Kaggle. [Online]. Available: https://www.kaggle.com/datasets/pypiahmad/indian-rupees-and-thai-baht-banknotes.
56. Keyush Nisar, "In the Wild Currency Images," Kaggle. [Online]. Available:https://www.kaggle.com/datasets/keyushnisar/in-the-wild-currency-images.
57. Mendeley Data, "Dataset," Mendeley Data. [Online]. Available:https://data.mendeley.com/datasets/xn44yz596n.
58. Shuvo Kumar Basak, "Bangladeshi Currency Old/New/Coins and Notes Dataset," Kaggle. [Online]. Available: https://www.kaggle.com/datasets/shuvokumarbasak2030/bangladeshi-currency-oldnewcoins- and-notes-dataset.
59. Data in Brief, "Article," Sci. Direct. [Online]. Available:https://www.sciencedirect.com/science/article/pii/S2352340925005785.
60. Tazwar Mohammed, "Augmented Bangla Money Dataset with 200 Taka," Kaggle. [Online]. Available:https://www.kaggle.com/datasets/tazwarmohammed/augmented-bangla-money-dataset-with-200-taka .
61. Ismail Ismail Tijjani, "Naira Nigerian Currency Dataset," Kaggle. [Online]. Available: https://www.kaggle.com/datasets/ismailismailtijjani/naira-nigerian-currency-datasetscribbr.
62. Aishwarya Techie, "USD Bill Classification Dataset," Kaggle. [Online]. Available:https://www.kaggle.com/datasets/aishwaryatechie/usd-bill-classification-dataset.
63. Sajjad FC13, "Bangla Currency Detection Using CNN," Kaggle. [Online]. Available: https://www.kaggle.com/code/sajjadfc13/bangla-currency-detection-using-cnn
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