Iraqi Currency Recognition System Using RGB and HSV Color Average



Volume 2, Issue 1
MAKERA M AZIZ

Published online: 29 February 2016 
Article Views: 16

Abstract

This paper proposed a method to recognize Iraqi currency by computing the average of each color (RGB) for each currency paper. The average of the color will compare with a database that already has been stored in the system. This database includes the average color of each currency. The comparison will use the correlation to find the minimum error. To improve the system and get more accurate results, the color system will convert to HSV system and use the same steps that applied to the RGB color system. Compare the result that we got from two color system to take the last decision. Matlab environment 2011a has been used in this system.

References

  1. Abbasi, A. A. (2014). A review on different currency recognition system for Bangladesh India China and Euro Currency. Research Journal of Applied Sciences, Engineering and Technology, 7(8), 1689-1690.
  2. Abdul Latef, A. A. (2012). Image retrieval based on coefficient correlation Index. Ibn Al-Haitham Journal for Pure and Applied Science, 25(2), 395-402.
  3. Ahmed, D. R., & Nordin , J. (2011). Offline OCR system for machine-printed turkish using template matching. Advanced Material Research, 341-342, 565-569.
  4. Banga, V. K., & Dadwal, M. (2012). Estimate ripeness level of fruits using RGB color space and fuzzy logic technique. International Journal of Engineering and Advanced Technology, 2(1), 225-229.
  5. Chary, R. R., Lakshmi, D. R., & Sunitha, K. N. (2012). Feature extraction methods for color image similarity. Advanced Computing: An International Journal, 3(2), 147-157.
  6. Debnath, K. K., Ahmed, S. U., & Shahjahan, M. (2010). A paper currency recognition system using negatively correlated neural network ensemble. Journal of Multimedia, 5(6), 560-567. https://dx.doi.org/10.4304/jmm.5.6.560-567
  7. Gogoi, M., Ali, S. E., & Mukherjee, S. (2015, February). Automatic Indian currency denomination recognition system based on artificial neural network. In Signal Processing and Integrated Networks (SPIN), 2015 2nd International Conference on (pp. 553-558). https://dx.doi.org/10.1109/spin.2015.7095416
  8. Gowri, S. (2012). Color and Texture Based Image Retrieval. ARPN Journal of Systems and Software, 2(1), 1-6.
  9. Grijalva, F., Rodríguez, J. C., Larco, J., & Orozco, L. (2010). Smartphone Recognition of the U.S. Banknotes Denomination, for Visually Impaired People. (pp. 1-6). Bogota. https://dx.doi.org/10.1109/andescon.2010.5631773
  10. Jain, V. K., & Vijay, R. (2013). Indian currency denomination identification using image processing technique. International Journal of Computer Science and Information Technologies, 4(1), 126-128.
  11. Jaswal, G., Kaul, A., & Parmar, R. (2012). Content based image retrieval using color space approaches. International Journal of Engineering and Advanced Technology, 2(1), 4-7.
  12. Kekre, H. B., Thepade, S. D., & Maloo, A. (2010). Query by image content using color-texture features extracted from Haar wavelet pyramid. IJCA Journal Special Issue on CASCT, 53-60. https://dx.doi.org/10.5120/1006-41
  13. Kodituwakku, S. R., & Selvarajah, S. (2010). Comparison of color features for image retrieval. Indian Journal of Computer Science and Engineering, 1(3), 207-211.
  14. Kumar, P., & Aggarwal, H. (2012). Indian currency note denomination recognition in color images. International Journal on Advanced Computer Engineering and Communication Technology, 1(1), 12-18.
  15. Kumar, V. V., Rao, N. G., Rao, A. N., & Krishna, V. V. (2009). IHBM: integrated histogram bin matching for similarity measures of color image retrieval. International Journal of Signal Processing, Image Processing and Pattern Recognition, 2(3), 109-120.
  16. Lamont, F. G., Cervantes, J., López, A., & Rodríguez, L. (2013). Classification of Mexican paper currency denomination by extracting their discriminative colors. 12th Mexican International Conference on Artificial Intelligence (pp. 403-412). Mexico City, Mexico: Springer-Verlag Berlin Heidelberg.
  17. Mirza, R., & Vinti, N. (2012). Paper currency verification system based on characteristic extraction using image processing. International Journal of Engineering and Advanced Technology, 3(1), 68-71.
  18. Pawade, D., Chaudhari, P., & Sonkamble, H. (2013). Comparative study of different paper currency and coin currency recognition method. International Journal of Computer Applications, 66(23), 26-31.
  19. Pawar, P. D., & Kale, S. B. (2012). Recognition of Indian currency note based on HSV parameters. International Journal of Science and Research, 3(6), 132-137.
  20. Roy, K., & Mukherjee, J. (2013). Image similarity measure using color histogram, color coherence vector, and sobel method. International Journal of Science and Research (IJSR), 2(1), 538-543.
  21. Siewer, I., Murray, I., & Dias, T. (2001). Australian currency note identifier for the vision impaired: Part I hardware description. Seventh Australian and New Zealand Intelligent Information Systems Conference (pp. 135-139). Perth, Western Australia.
  22. Yadav, B. P., Patil, C. S., Karhe, R. R., & Patil, P. H. (2014). Indian currency recognition and verification system using image processing. International Journal of Advanced Research in Computer Science and Software Engineering, 4(12), 943-947.
  23. Yaseri, A., & Anisheh, S. M. (2013). A novel paper currency recognition using Fourier Mellin transform, hidden Markov model and support Vector machine. International Journal of Computer Applications, 61(7), 17-22. https://dx.doi.org/10.5120/9939-3997

To Cite this article

Aziz, M. M. (2016). Iraqi currency recognition system using RGB and HSV color average. International Journal of Business and Administrative Studies, 2(1), 9-15.