Bank note recognition for the vision impaired

  • A. Hinwood
  • P. Preston
  • G. J. Suaning
  • N. H. Lovell
Technical Note


Blind Australians find great difficulty in recognising bank notes. Each note has the same feel, with no Braille markings, irregular edges or other tangible features. In Australia, there is only one device available that can assist blind people recognise their notes. Internationally, there are devices available; however they are expensive, complex and have not been developed to cater for Australian currency. This paper discusses a new device, the MoneyTalker that takes advantage of the largely different colours and patterns on each Australian bank note and recognises the notes electronically, using the reflection and transmission properties of light. Different coloured lights are transmitted through the inserted note and the corresponding sensors detect distinct ranges of values depending on the colour of the note. Various classification algorithms were studied and the final algorithm was chosen based on accuracy and speed of recognition. The MoneyTalker has shown an accuracy of more than 99%. A blind subject has tested the device and believes that it is usable, compact and affordable. Based on the devices that are available currently in Australia, the MoneyTalker is an effective alternative in terms of accuracy and usability.

Key words

vision impairment technical aid disabled classification 


  1. 1.
    Reserve Bank of Australia.How the RBA assists people with a vision impairment to differentiate notes. accessed 7/6/2005.Google Scholar
  2. 2.
    Congdon, N., O’Colmain, B., Klaver, C.C., Klein, R., Munoz, B., Friedman, D.S., Kempen, J., Taylor, H.R. and Mitchell, P.,Causes and prevalence of visual impairment among adults in the United States. Archives of Ophthalmology. 122(4):477–85, 2004.CrossRefPubMedGoogle Scholar
  3. 3.
    ABC Radio National,Money Talks in M. Holstrom, ed., The Buzz, 2004.Google Scholar
  4. 4.
    Bryenton, E.L., Brule, D.A. and Bryenton, A.L.,Portable hand-held banknote reader, US patent 5692068, 1997.Google Scholar
  5. 5.
    Witten, I.H. and Frank, E.,Data mining: practical machine learning tools and techniques with Java implementations, Morgan Kaufmann, San Francisco, USA, 2000.Google Scholar
  6. 6.
    Aha, D.W., Kibler, D. and Albert, M.K.,Instance-based learning algorithms, Machine Learning (Historical Archive), 6, pp. 37–66, 1997.Google Scholar
  7. 7.
    Cleary, J.G. and Trigg, L.E. K*.,An instance-based learner using an entropic distance measure, 12th International Conference on Machine learning, Morgan Kaufmann, Tahoe City, California, US, pp 108–114, 1995.Google Scholar
  8. 8.
    Russell, S.J. and Norvig, P.,Artificial intelligence: a modern approach, Prentice Hall, Upper Saddle River, USA, 2003.Google Scholar
  9. 9.
    Landwehr, N., Hall, M. and Frank, E.,Logistic model trees, Proc Fourteenth European Conference on Machine Learning, LNCS 2837, Cavtat-Dubrovnik, Croatia, pp 241-252, 2003.Google Scholar
  10. 10.
    Melville, P. and Mooney, R.J.,Constructing diverse classifier ensembles using artificial training examples, IJCAI, Acapulco, Mexico, pp 505–510, 2003.Google Scholar
  11. 11.
    Royal Australian Mint.Mint issue, vol 59, 2004.Google Scholar

Copyright information

© Australasian College of Physical Scientists and Engineers in Medicine 2006

Authors and Affiliations

  • A. Hinwood
    • 3
  • P. Preston
    • 3
  • G. J. Suaning
    • 2
    • 3
  • N. H. Lovell
    • 3
    • 1
  1. 1.National Information and Communications Technology Australia (NICTA) Australian Technology ParkEveleighAustralia
  2. 2.School of EngineeringUniversity of NewcastleNewcastleAustralia
  3. 3.Graduate School of Biomedical EngineeringUniversity of New South WalesSydneyAustralia

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