Abstract
Most of the time the people are finding it difficult to recognize the paper currencies due to their inability to see. Also in real life, it is not possible to trust the people to help a visual impaired person in recognizing the paper currency. But, in this regard, we can always trust an application that is easy to access and trust worthy. For building the application for the same, we need the help of machine learning algorithms. Several algorithms of machine learning such as supervised, unsupervised, semi-supervised, and reinforcement learning are available. Our paper mainly focuses on the recognition of different currency notes for the visually impaired people by using machine learning and taking the model as ResNet50 which is a 50 layers deep convolutional neural network. We have also used image processing by resizing and crop** the image to extract the important features of the notes so that the machine can recognize the currency notes efficiently. Our main contribution in this paper is through using various methods of image processing and feeding those processed image in our model, so that this model gets a fairly high accuracy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
References
Pathak A, Aurelia S (2020) Mobile-based Indian currency detection model for the visually impaired. In: Convergence of ICT and smart devices for emerging applications. Springer, Cham, pp 67–79
Patil MV, Vishal MA, Ajay Sirsat (2020) Artificial intelligence digital assistant for visually impaired people. Int J Fut Gen Commun Network 13(1):24–31
Mirza R, Nanda V (2012) Paper currency verification system based on characteristic extraction using image processing. Int J Eng Adv Technol (IJEAT) 1(3):68–71
Ciocca G et al (2007) Self-adaptive image crop** for small displays. IEEE Trans Consum Electron 53(4):1622–1627
Yang, Qiang, et al. “Federated machine learning: Concept and applications.“ ACM Transactions on Intelligent Systems and Technology (TIST) 10.2 (2019): 1–19.
Ghosh D, Singh J (2020) A novel approach of software fault prediction using deep learning technique. In: Automated software engineering: a deep learning-based approach. Springer, Cham, pp 73–91
Singh J, Sahoo B (2011) Software effort estimation with different artificial neural network
Li L et al (2020) A review of face recognition technology. IEEE Access 8:139110–139120
Zhang Q, Yan WQ (2018) Currency detection and recognition based on deep learning. In: 2018 15th IEEE international conference on advanced video and signal based surveillance (AVSS). IEEE
Chakraborty K et al (2013) Recent developments in paper currency recognition system. Int J Res Eng Technol 2:222–226
Kamesh DBK et al (2016) Camera based text to speech conversion, obstacle and currency detection for blind persons. Indian J Sci Technol 9(30):1–5
Vishnu R, Bini Omman (2014) Currency detection using similarity indices method. In: International conference for convergence for technology-2014. IEEE
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Maity, A., Roy, S.G., Bhattacharjee, S., Dutta, P., Singh, J. (2023). Prediction of Indian Currency for Visually Impaired People Using Machine Learning. In: Asari, V.K., Singh, V., Rajasekaran, R., Patel, R.B. (eds) Computational Methods and Data Engineering. Lecture Notes on Data Engineering and Communications Technologies, vol 139. Springer, Singapore. https://doi.org/10.1007/978-981-19-3015-7_19
Download citation
DOI: https://doi.org/10.1007/978-981-19-3015-7_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-3014-0
Online ISBN: 978-981-19-3015-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)