Published March 8, 2026 | Version 1
Journal article Open

A Systematic Review of Currency Detection Technologies: Global Developments and Future Prospects for Bangladesh 2020-2025

Authors/Creators

  • 1. ROR icon University of Global Village (UGV)

Contributors

  • 1. ROR icon University of Global Village (UGV)

Description

This thesis provides a systematic review of currency recognition technologies, with a focus on
global progress and its applicability to Bangladesh. Based on the PRISMA protocol, 37 highquality studies were shortlisted from more than 200 papers published between 2020 and 2025.
The systematic review groups global progress in recognition, counterfeit detection, hybrid
models, and deployment strategies. Globally, the transition from handcrafted features and
traditional image processing to deep learning models like CNNs, YOLO, and Vision
Transformers is apparent. In Bangladesh, progress has been made in lightweight CNNs and
publicly available datasets such as BanglaTaka, NSTU-BDTAKA, and JaalTaka, but
challenges remain in the size of datasets, multimodal fusion, and real-world applications. The
major gaps in the literature are the absence of benchmarking, very little work on the integration
of explainability tools like SHAP and GradCAM, and very few mobile-friendly systems. This
thesis proposes a comprehensive framework that integrates recognition and counterfeit
detection, hybrid models, and deployment strategies specifically for Bangladesh.

Keywords: Currency Detection, Banknote Recognition, Deep Learning, Comparative
Analysis, Machine Learning, Bangladesh Currency.

Files

A Systematic Review of Currency Detection Technologies.pdf

Files (807.6 kB)

Additional details

Dates

Issued
2026-03-08
Initial submission of the research project.

Software

Programming language
Python
Development Status
Concept

References

  • M. A. Siddiki, M. N. Hossain, K. Akhter, and M. R. Rahman, "Enhanced Counterfeit Detection of Bangladesh Currency through CNNs," IJIRCST, vol. 12, no. 2, Mar. 2024, doi: 10.55524/ijircst.2024.12.2.1.
  • A. Pathak, A. Chakraborty, M. Rahaman, T. S. Rafa, and U. Nayema, "Bangladeshi Currency Authentication Checking System Using Convolutional Neural Networks," in Proc. Int. Conf., Nov. 2024, doi: 10.1007/978-981-97-6726-7_20
  • M. N. I. Nuhash and S. Akter, "BanglaTaka: A Dataset for Classification of Bangladeshi Banknotes," Data in Brief, vol. 111853, 2025, doi: 10.1016/j.dib.2025.111853.
  • M. T. Islam, M. Ahmad, and A. S. Bappy, "Real-Time Bangladeshi Currency Recognition Using Faster R-CNN Approach for Visually Impaired People," in ICCIS 2020 (Springer), 2021, doi: 10.1007/978-981-16-1089-9_13.
  • R. Tasnim et al., "Bangladeshi Banknote Recognition in Real-time using CNN for Visually Impaired People," in IEEE ICREST, 2021.