Skip to main content
Log in

Coinnet: platform independent application to recognize Indian currency notes using deep learning techniques

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In India, nearly 12 million visually impaired people had difficulty in identifying the currency notes. There is a need to develop an application that can recognize the currency note and provide a vocal message. In this paper, a novel lightweight Convolutional Neural Network (CNN) model is developed for efficient web and mobile applications to recognize the Indian currency notes. A new dataset for Indian currency notes has been created to train, validate, and test the CNN model. This CNN based web and mobile applications will provide a text and audio output based on the recognized currency note. The proposed model is developed using TensorFlow and improved by selection of optimal hyperparameter value, and compared with existing well known CNN architectures using transfer learning. Based on the results it has been observed that proposed model perform well over six widely used existing architectures in terms of training and testing accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23

Similar content being viewed by others

References

  1. Ahmadi A, Omatu S, Fujinaka T, Kosaka T (2004) A reliable method for classification of bank notes using artificial neural networks. Artif Life Robot 8(2):133–139

    Article  Google Scholar 

  2. Aoba M, Kikuchi T, Takefuji Y (2003) Euro banknote recognition system using a three-layered perceptron and rbf networks. IPSJ Trans Math Model Appl 44:99–109

    Google Scholar 

  3. Awoyera P, Akinmusuru J, Krishna AS, Gobinath R, Arunkumar B, Sangeetha G (2020) Model development for strength properties of laterized concrete using artificial neural network principles. In: Soft computing for problem solving. Springer, pp 197–207

  4. Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251–1258

  5. Debnath KK, Ahmed SU, Shahjahan M, Murase K (2010) A paper currency recognition system using negatively correlated neural network ensemble. J Multimed 5 (6):560

    Article  Google Scholar 

  6. Dhandapani K, Venugopal K, Kumar JV (2019) Ecofriendly and green synthesis of carbon nanoparticles from rice bran: characterization and identification using image processing technique. Int J Plast Technol, 1–11

  7. Doush IA, Sahar AB (2017) Currency recognition using a smartphone: comparison between color sift and gray scale sift algorithms. J King Saud Univ-Comput Inform Sci 29(4):484–492

    Google Scholar 

  8. Feng BY, Ren M, Zhang X, Suen CY (2014) Automatic recognition of serial numbers in bank notes. Pattern Recogn 47(8):2621–2634

    Article  Google Scholar 

  9. Frosini A, Gori M, Priami P (1996) A neural network-based model for paper currency recognition and verification. IEEE Trans Neural Netw 7(6):1482–1490

    Article  Google Scholar 

  10. García-Lamont F, Cervantes J, López A (2012) Recognition of mexican banknotes via their color and texture features. Expert Syst Appl 39(10):9651–9660

    Article  Google Scholar 

  11. Gogoi M, Ali SE, Mukherjee S (2015) Automatic indian currency denomination recognition system based on artificial neural network. In: 2015 2nd international conference on signal processing and integrated networks (SPIN). IEEE, pp 553–558

  12. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  13. Hlaing KNN, Gopalakrishnan AK (2016) Myanmar paper currency recognition using glcm and k-nn. In: 2016 Second Asian conference on defence technology (ACDT). IEEE, pp 67–72

  14. https://github.com/10zinten/Indian-Currency-Recognition (2019 (accessed April 17, 2019))

  15. https://github.com/keras-team/keras-applications/blob/master/ (2019 (accessed April 30, 2019))

  16. https://github.com/soum-io/TensorFlowLiteInceptionTutorial (2019 (accessed April 30, 2019))

  17. https://medium.com/dustindavignon/upload-multiple-images-with-python-flask-and-flask-dropzone-d5b821829b1d (2019 (accessed April 30, 2019))

  18. https://www.thehindubusinessline.com/money-and-banking/rbi-proposes-mobile-app-to-help-visually-impaired-to-identify-currency-notes/article27108259.ece (2019 (accessed May 12, 2019))

  19. Kagehiro T, Nagayoshi H, Sako H (2006) A hierarchical classification method for us bank-notes. IEICE Trans Inform Syst 89(7):2061–2067

    Article  Google Scholar 

  20. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  21. Kumar SN, Kumar PP, Sandeep C, Shwetha S (2018) Opportunities for applying deep learning networks to tumour classification. Indian J Public Health Res Develop 9(11):742–747

    Article  Google Scholar 

  22. Kumar BA, Sangeetha G, Srinivas A, Awoyera P, Gobinath R, Ramana VV (2020) Models for predictions of mechanical properties of low-density self-compacting concrete prepared from mineral admixtures and pumice stone. In: Soft computing for problem solving. Springer, pp 677– 690

  23. Liu X (2008) A camera phone based currency reader for the visually impaired. In: Proceedings of the 10th international ACM SIGACCESS conference on Computers and accessibility. ACM, pp 305–306

  24. Liu Y, Zeng L, Huang Y (2014) Haar-svm for real-time banknotes recognition. J Inform Comput Sci 11(12):4031–4039

    Article  Google Scholar 

  25. Mittal S, Mittal S (2018) Indian banknote recognition using convolutional neural network. In: 2018 3rd International conference on internet of things: smart innovation and usages (IoT-SIU). IEEE , pp 1–6

  26. Paisios N, Rubinsteyn A, Vyas V, Subramanian L (2011) Recognizing currency bills using a mobile phone: an assistive aid for the visually impaired. In: Proceedings of the 24th annual ACM symposium adjunct on User interface software and technology. ACM, pp 19–20

  27. Papastavrou S, Hadjiachilleos D, Stylianou G (2010) Blind-folded recognition of bank notes on the mobile phone. In: ACM SIGGRAPH 2010 Posters. ACM, p 68

  28. Parlouar R, Dramas F, Macé MM, Jouffrais C (2009) Assistive device for the blind based on object recognition: an application to identify currency bills. In: Proceedings of the 11th international ACM SIGACCESS conference on Computers and accessibility. ACM, pp 227–228

  29. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556

  30. Singh S, Choudhury S, Vishal K, Jawahar C (2014) Currency recognition on mobile phones. In: 2014 22nd international conference on pattern recognition. IEEE, pp 2661–2666

  31. Stein C, Nickel C, Busch C (2012) Fingerphoto recognition with smartphone cameras. In: 2012 BIOSIG-Proceedings of the international conference of biometrics special interest group (BIOSIG). IEEE, pp 1–12

  32. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826

  33. Verma K, Singh BK, Agarwal A (2011) Indian currency recognition based on texture analysis. In: 2011 Nirma University international conference on engineering. IEEE, pp 1–5

  34. Yan WQ, Chambers J, Garhwal A (2015) An empirical approach for currency identification. Multimed Tools Appl 74(13):4723–4733

    Article  Google Scholar 

  35. Zeggeye JF, Assabie Y (2016) Automatic recognition and counterfeit detection of ethiopian paper currency. Int J Image Graph Signal Process, 8(2)

Download references

Acknowledgements

We thanks Leadingindia.ai and Bennett University for providing supercomputer NVIDIA DGX V100 access to do this work. We also acknowledge Dr Deepak Garg (Director, LeadingIndia.ai) and Dr Suneet K. Gupta (Assistant Professor, Bennett University) for giving us the environment to work on this specific domain. I would also like to show our gratitude to S.R. Engineering College for giving permission with financial support to do this work at Bennett University. If anyone needs complete code and Indian currency notes dataset, please mail to the corresponding authors.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Venkataramana Veeramsetty.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Veeramsetty, V., Singal, G. & Badal, T. Coinnet: platform independent application to recognize Indian currency notes using deep learning techniques. Multimed Tools Appl 79, 22569–22594 (2020). https://doi.org/10.1007/s11042-020-09031-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09031-0

Keywords

Navigation