Image Based Ringgit Banknote Recognition for Visually Impaired

Authors

  • N. A. Jasmin Sufri Faculty of Biosciences and Medical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia.
  • N. A. Rahmad Faculty of Biosciences and Medical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia.
  • M. A. As’ari Faculty of Biosciences and Medical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia. Sport Innovation and Technology Center (SITC), Institute of Human Centered Engineering (IHCE), Universiti Teknologi Malaysia, Johor Bahru, Malaysia.
  • N. A. Zakaria Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia.
  • M. N. Jamaludin Faculty of Biosciences and Medical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia. Sport Innovation and Technology Center (SITC), Institute of Human Centered Engineering (IHCE), Universiti Teknologi Malaysia, Johor Bahru, Malaysia.
  • L. H. Ismail
  • N. H. Mahmood Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia.

Keywords:

Banknotes Recognition, Cross-validation, Decision Tree Classifier, k-Nearest Neighbors,

Abstract

Visually impaired people face a number of difficulties in order to interact with the environment because most of the information encoded is visual. Visual impaired people faced a problem in identifying and recognizing the different currency. There are many devices available in the market but not acceptable to detect Malaysian ringgit banknote and very pricey. Many studies and investigation have been done in introducing automated bank note recognition system and can be separated into vision based system or sensor based system. The objective of this project was to develop an automated system or algorithm that can recognize and classify different Ringgit Banknote for visually impaired person based on banknote image. In this project, the features extraction of the RGB values in six different classes of banknotes (RM1, RM5, RM10, RM20, RM 50, and RM100) was done by using Matlab software. Three features called RB, RG and GB extracted from the RGB values were used for the classification algorithms such as k-Nearest Neighbors (k-NN) and Decision Tree Classifier (DTC) for recognizing each classes of banknote. Ten-fold cross validation was used to select the optimized k-NN and DTC, which was based on the smallest cross validation loss. After that, the performance of optimize k-NN and DTC model was presented in confusion matrix. Result shows that the proposed k-NN and DTC model managed to achieve 99.7% accuracy with the RM50 class causing major reduction in performance. In conclusion, an image based automated system that can recognize the Malaysian banknote using k-NN and DTC classifier has been successfully developed.

References

O. U. Press, New Oxford English-Malay Dictionary Second Edition, . Shah Alam: Oxford Fajar Sdn. Bhd, 2009.

R. Velázquez, "Wearable assistive devices for the blind," in Wearable and Autonomous Biomedical Devices and Systems for Smart Environment. Lecture Notes in Electrical Engineering, vol. 75, A. Lay-Ekuakille & S.C. Mukhopadhyay, Eds., Berlin, Heidelberg: Springer, 2010, pp. 331-349.

J. H. Roy Shilkrot , M. E. Wong, P. Maes, S. Nanayakkara, "“FingerReader: a wearable device to explore printed text on the go,” in Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, Seoul, Korea, 2015, pp. 2363-2372.

A. Kulkarni and K. Bhurchandi, "Low Cost e-book reading device for blind people," in 2015 International Conference on Computing Communication Control and Automation (ICCUBEA), India, 2015, pp. 516-520.

F. M. Hasanuzzaman, X. Yang, and Y. Tian, "Robust and effective component-based banknote recognition for the blind," IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 42, pp. 1021-1030, 2012.

H. Aggarwal and P. Kumar, "Indian currency note denomination recognition in color images," International Journal on Advanced Computer Engineering and Communication Technology, vol. 1, pp. 12-18, 2012.

Z. Solymár, A. Stubendek, M. Radványi and K. Karacs, "Banknote recognition for visually impaired," in 2011 20th European Conference on Circuit Theory and Design (ECCTD), Linköping, Sweden, 2011, pp. 841-844.

A. R. Domínguez, C. Lara-Alvarez, and E. Bayro, "Automated banknote identification method for the visually impaired," in Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: CIARP 2014. Lecture Notes in Computer Science, vol. 8827, E. Bayro-Corrochano and E. Hancock, Eds., Cham: Springer, 2014, pp. 572-579.

D. Mulmule and A. Dravid, "A study of computer vision techniques for currency recognition on mobile phone for the visually impaired " International Journal of Advanced Research in Computer Science and Software Engineering, vol. 4, pp. 160-165, Nov 2014.

S. Gai, G. Yang, and M. Wan, "Employing quaternion wavelet transform for banknote classification," Neurocomputing, vol. 118, pp. 171-178, 2013.

S. Gai, "New banknote defect detection algorithm using quaternion wavelet transform," Neurocomputing, vol. 196, pp. 133-139, 2016.

N. A. Semary, S. M. Fadl, M. S. Essa, and A. F. Gad, "Currency recognition system for visually impaired: Egyptian banknote as a study case," in 2015 5th International Conference on Information & Communication Technology and Accessibility (ICTA), Morocco, 2015, pp. 1-6.

F. Grijalva, J. C. Rodriguez, J. Larco and L. Orozco, "Smartphone recognition of the U.S. banknotes' denomination, for visually impaired people," in ANDESCON, 2010 IEEE, Columbia, 2010, pp. 1-6.

A. Mohamed, M. I. Ishak, and N. Buniyamin, "Development of a malaysian currency note recognizer for the vision impaired," in Engineering and Technology (S-CET), 2012 Spring Congress on, 2012, pp. 1-4.

K. Wickramasinghe and D. D. Silva, "Bank notes recognition device for Sri Lankan vision impaired community," in 2013 8th International Conference on Computer Science & Education (ICCSE), Sri Lanka, 2013, pp. 609-612.

L. Hakobyan, J. Lumsden, D. O’Sullivan and H. Bartlett, "Mobile assistive technologies for the visually impaired," Survey of Ophthalmology, vol. 58, pp. 513-528, Nov 2013.

Colenbrander, "Aspects of vision loss-visual functions and functional vision," Vis. Impair. Res, vol. 5, pp. 115-136, 2003.

ISO, "Assistive Products for Persons with Disability-Classification and Terminology," ed, 2011.

D. Abdul Rasool and S. Sabra, "Mobile-embedded smart guide for the blind," in Digital Information and Communication Technology and Its Applications. DICTAP 2011. Communications in Computer and Information Science, vol 167, H. Cherifi, J. M. Zain, and E. ElQawasmeh, Eds., Berlin Heidelberg: Springer, 2011, pp. 571-578.

S. Abdullah, N. M. Noor, and M. Z. Ghazali, "Mobility recognition system for the visually impaired," in 2014 IEEE 2nd International Symposium on Telecommunication Technologies (ISTT), Malaysia, 2014, pp. 362-367.

Y. H. Lee and G. Medioni, "RGB-D camera based wearable navigation system for the visually impaired," Computer Vision and Image Understanding, vol. 149, pp. 3-20, Aug 2016.

Downloads

Published

2017-12-04

How to Cite

Sufri, N. A. J., Rahmad, N. A., As’ari, M. A., Zakaria, N. A., Jamaludin, M. N., Ismail, L. H., & Mahmood, N. H. (2017). Image Based Ringgit Banknote Recognition for Visually Impaired. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(3-9), 103–111. Retrieved from https://jtec.utem.edu.my/jtec/article/view/3133

Issue

Vol. 9 No. 3-9: Biomedical Electronics and Informatics

Section

Articles

License

TRANSFER OF COPYRIGHT AGREEMENT

The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):

  1. All proprietary right other than copyright, such as patent rights
  2. The right to make further copies of all or part of the published article for my use in classroom teaching
  3. The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
  4. The right to make copies of the published work for internal distribution within the institution that employs me

I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.