Skip to main content
SpringerLink
Log in
Book cover

Convergence of ICT and Smart Devices for Emerging Applications pp 67–79Cite as

Mobile-Based Indian Currency Detection Model for the Visually Impaired

  • Chapter
  • First Online:

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

According to surveys held in 2019, India holds the largest population standing just after China, but when it comes to visually impaired people, India ranks number one. There are approximately 37 million people across India who are suffering from visual impairment. Special care and measures are taken to help these people live a peaceful life as any other citizen of India, but with the demonetization that happened in the recent years, the Indian economy was replaced with newer currency notes as an attempt to stop black money and fight corruption. Even though the objectives were clear and attainable, with the newer currency notes, the visually impaired people are facing various problems, as there is no provision for them to actually check the currency as the notes are not equipped with Braille system and the sizes of each and every currency is also the same in many cases. To counteract this problem, a mobile-based Indian currency detection model would be a better solution as it enables a visually impaired person to identify the value of specific currency he is holding. The mobile-based Indian currency detection model is the proposed model which will be using image processing for feature extraction and a basic CNN (convolutional neural network) for identification of currency with the given feature inputs. This model is being made into a mobile-based application so as to enable a visually impaired person to check for any possible frauds as fast as possible.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Guo, T., Dong, J., Li, H., & Gao, Y. (2017). Simple convolutional neural network on image classification. In 2017 IEEE 2nd international conference on Big Data Analysis (ICBDA) (pp. 721–724). Beijing.

    Google Scholar 

  2. Rathee, N., Kadian, A., Sachdeva, R., Dalel, V., & Jaie, Y. (2016). Feature fusion for fake Indian currency detection. In 2016 3rd international conference on Computing for Sustainable Global Development (pp. 1265–1270). New Delhi: INDIACom.

    Google Scholar 

  3. Darade, S. R., & Gidveer, G. R. (2016). Automatic recognition of fake Indian currency note. In 2016 international conference on Electrical Power and Energy Systems (ICEPES) (pp. 290–294). Bhopal.

    Google Scholar 

  4. Pissaloux, E. E. (2002). A vision system design for blinds mobility assistance. In Proceedings of the second joint 24th annual conference and the annual fall meeting of the Biomedical Engineering Society Engineering in Medicine and Biology (Vol. 3, pp. 2349–2350). Houston, TX.

    Google Scholar 

  5. Takatori, N., Nojima, K., Matsumoto, M., Yanashima, K., & Magatani, K. (2006). Development of voice navigation system for the visually impaired by using IC tags. In 2006 international conference of the IEEE Engineering in Medicine and Biology Society (pp. 5181–5184). New York, NY.

    Google Scholar 

  6. Hassanpour, H., Yaseri, A., & Ardeshiri, G. Feature extraction for paper currency recognition, 1-4244-0779-6/07/$20.00 ©2007 IEEE.

    Google Scholar 

  7. Takeda, F., & Omatu, S. (1995). High speed papercurrency recognition by neural networks. IEEE Transaction on Neural Networks, 6(1), 73–77.

    Google Scholar 

  8. Alfarras, M. (2012). Bahraini paper currency recognition. Journal of Advanced Computer Science and Technology Research, 2(2), 104–115.

    Google Scholar 

  9. Vishnu, R., & Omman, B. (2014). Principal features for Indian currency recognition. In Annual IEEE India conference.

    Google Scholar 

  10. Bhavani, R., Karthikeyan, A., & Novel, A. (2014, April). Method for banknote recognition system. IJCSE, 2(4), 165–167.

    Google Scholar 

  11. Rajan, G. V., Panicker, D. M., Chacko, N. E., Mohan, J., & Kavitha, V. K. (2018). An extensive study on currency recognition system using image processing. In 2018 conference on Emerging Devices and Smart Systems (ICEDSS) (pp. 228–230). Tiruchengode.

    Google Scholar 

  12. Ballado, A. H., et al. (2015). Philippine currency paper bill counterfeit detection through image processing using Canny Edge Technology. In 2015 international conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM) (pp. 1–4). Cebu City.

    Google Scholar 

  13. Sarfraz, M. (2015). An intelligent paper currency recognition system. Procedia Computer Science, 65, 538–545.

    Google Scholar 

  14. Konecki, M., Ivković, N., & Kaniški, M. (2016). Making programming education more accessible for visually impaired. In 2016 39th international convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (pp. 887–890). Opatija.

    Google Scholar 

  15. Kulkarni, A., Wang, A., Urbina, L., Steinfeld, A., & Dias, B. (2016). Robotic assistance in indoor navigation for people who are blind. In 2016 11th ACM/IEEE international conference on Human-Robot Interaction (HRI) (pp. 461–462). Christchurch.

    Google Scholar 

  16. Soysa, L., Lokuge, K., Wimalasundera, I., & De Silva, M. N. (2010). Enhancing learning for Visually Impaired with technology: MATHVIS. In 2010 international conference on Technology for Education (pp. 228–229). Mumbai.

    Google Scholar 

  17. Rao, S. N., & Suraj, R. (2016). Smartphone-aided reconfigurable multi-device controller system for the visually challenged. In 2016 IEEE international conference on Computational Intelligence and Computing Research (ICCIC) (pp. 1–4). Chennai.

    Google Scholar 

  18. Froneman, T., van den Heever, D., & Dellimore, K. (2017). Development of a wearable support system to aid the visually impaired in independent mobilization and navigation. In 2017 39th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 783–786). Seogwipo.

    Google Scholar 

  19. Alzubi, J., Nayyar, A., & Kumar, A. (2018, November). Machine learning from theory to algorithms: An overview. Journal of Physics: Conference Series, 1142(1), 012012. IOP Publishing.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India

    Abhishek Pathak & Sagaya Aurelia

Authors
  1. Abhishek Pathak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sagaya Aurelia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abhishek Pathak .

Editor information

Editors and Affiliations

  1. School of Technology and Management, Inst Politécnico de Viana do Castelo, Viana do Castelo, Portugal

    Sara Paiva

  2. Department of ECE, School of Electronics, Computer Science and Informatics, Haldia Institute of Technology, Haldia, West Bengal, India

    Suman Paul

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Pathak, A., Aurelia, S. (2020). Mobile-Based Indian Currency Detection Model for the Visually Impaired. In: Paiva, S., Paul, S. (eds) Convergence of ICT and Smart Devices for Emerging Applications. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-41368-2_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-41368-2_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-41367-5

  • Online ISBN: 978-3-030-41368-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation