Autors: Nayef M., Nikolova, I. N., Zapryanov, G. S.
Title: Bulgarian Banknotes Denomination Classification using Transfer Learning
Keywords: banknote recognition, CNN, deep learning, image classification, transfer learning

Abstract: Since the rise of Artificial Intelligence, image classification has played a pivotal role in assistive technologies for aiding the visually impaired. This research paper explores the application of transfer learning for the classification of Bulgarian banknote images, aiming to develop an effective and efficient method for identifying various denominations, and its potential for integration into assistive technologies. The study highlights the benefits of transfer learning in data-scarce scenarios by showing how it improves the performance and accuracy of specialized image classification.

References

  1. Ackland, P., Resnikoff, S., & Bourne, R., “World blindness and visual impairment: despite many successes, the problem is growing,” Community eye health, 30(100), 2017, p. 71.
  2. World Health Organization, “Blindness and vision impairment,” www.who.int.https://www.who.int/news-room/factsheets/detail/blindness-and-visual-impairment (accessed, May 2024)
  3. Hakobyan, L., Lumsden, J., O’Sullivan, D., & Bartlett, H., „Mobile assistive technologies for the visually impaired. Survey of ophthalmology,” 58(6), 2013, pp. 513-528.
  4. Donges, N., “What is transfer learning? exploring the popular deep learning approach,” builtin.com. https://builtin.com/data-science/transfer-learning (accessed, May 2024).
  5. Bulgarian Banknote Recognition Standard, “The security features of Bulgarian Circulating Banknotes,” www.bnb.bg.https://www.bnb.bg/bnbweb/groups/public/documents/bnb_download/po_im_money_sec_en.pdf (accessed, May, 2024).
  6. Lee, J. W., Hong, H. G., Kim, K. W., & Park, K. R., “A survey on banknote recognition methods by various sensors,” Sensors, 17(2), 2017, p. 313.
  7. Bruna, A., Farinella, G. M., Guarnera, G. C., & Battiato, S., “Forgery detection and value identification of Euro banknotes,” Sensors, 13(2), 2013, pp. 2515-2529.
  8. Sadyk, U., Turan, C., & Baimukashev, R., “Overview of deep learning models for banknote recognition,” in 2023 17th IEEE International Conference on Electronics Computer and Computation (ICECCO), 2023, pp. 1-5.
  9. Sufri, N. A. J., Rahmad, N. A., Ghazali, N. F., Shahar, N., & As’Ari, M. A., “Vision based system for banknote recognition using different machine learning and deep learning approach,” in 2019 IEEE 10th control and system graduate research colloquium (ICSGRC), 2019, pp. 5-8.
  10. Trinh, H. C., Vo, H. T., Pham, V. H., Nath, B., & Hoang, V. D., “Currency recognition based on deep feature selection and classification in Intelligent Information and Database Systems,” Proceedings of the 12th Asian Conference, ACIIDS, Phuket, Thailand, Springer Singapore, March 23–26, 2020, pp. 273-281.
  11. Aseffa, D. T., Kalla, H., & Mishra, S., “Ethiopian banknote recognition using convolutional neural network and its prototype development using embedded platform,” Journal of Sensors, no 1, 2022, p. 4505089
  12. Tasnim, R., Pritha, S. T., Das, A., & Dey, A., “Bangladeshi banknote recognition in real-time using convolutional neural network for visually impaired people,” in 2ndIEEE International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), 2021, pp. 388-393.
  13. de Goma, J., Rabano, M. J., Sanson, D. M., & Tadena, K. A., “An Integration of Transfer Learning in Modern Philippine Banknote Feature Detection,” Proceedings of the 7th International Conference on Computers in Management and Business, 2024, pp. 80-85.
  14. Oviedo, Felipe, Srinivas Vinnakota, Eugene Seleznev, Hemant Malhotra, Saqib Shaikh, and Juan Lavista Ferres, "BankNote-Net: Open dataset for assistive universal currency recognition," arXiv preprint arXiv:2204.03738, 2022.
  15. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C., “Mobilenetv2: Inverted residuals and linear bottlenecks,” Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 4510-4520.
  16. Meshram, V., Patil, K., & Meshram, V., “Evaluation of top pretrained models using transfer learning on banknote dataset with quality parameter,” Ingénierie des Systèmes d'Information, 28(3), 2023, p. 693.
  17. "Sampling ImageNet," planspace.org. https://planspace.org/20170430-sampling_imagenet/, (accessed, May 2024).
  18. “Open Source Computer Vision Library,” docs.opencv.org. https://docs.opencv.org/4.10.0/ (accessed, May 2024).
  19. “TensorFlow Lite convertor,” www.tensorflow.org . https://www.tensorflow.org/lite/ (accessed, May 2024).

Issue

2024 12th International Scientific Conference on Computer Science, COMSCI 2024 - Proceedings, 2024, Bulgaria, https://doi.org/10.1109/COMSCI63166.2024.10778514

Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus