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Vision based System for Optical Number Recognition

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International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 37 - Number 1
Year of Publication: 2012
Authors:
Anand B. Deshpande
10.5120/4574-6576

Anand B Deshpande. Article: Vision based System for Optical Number Recognition. International Journal of Computer Applications 37(1):32-34, January 2012. Full text available. BibTeX

@article{key:article,
	author = {Anand B. Deshpande},
	title = {Article: Vision based System for Optical Number Recognition},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {37},
	number = {1},
	pages = {32-34},
	month = {January},
	note = {Full text available}
}

Abstract

Vision based system for Optical Number recognition (ONR) deals with the recognition of processed numbers rather than magnetically processed ones. ONR is a process of automatic recognition of numbers by computers in images and digitized pages of text. ONR is one of the most fascinating and challenging areas of pattern recognition with various practical applications. It can contribute immensely to the advancement of an automation process and can improve the interface between man and machine in many applications. Moments and functions of moments have been extensively employed as invariant global features of images in pattern recognition. This paper shows the implementation and analysis of ONR, regardless of orientation, size and position, feature vectors are computed with the help of statistical moments.

References

  • Graham Leedham, Chen Yan, Kalyan Takru, Joie Hadi Nata Tan and Li Mian, “Comparison of Some Thresholding Algorithms for Text/Background Segmentation in Difficult Document Images”, Proceedings of the Seventh International Conference on Document Analysis and Recognition (ICDAR 2003)
  • M. K. Hu, “Visual pattern recognition by moment invariants,” IRE Trans. Information Theory, Vol.IT-8, pp.179-187, February 1962.
  • S. Pakchalakis and P. Lee, “Pattern recognition in gray level images using moment based invariant features,” Image Processing and its Applications, IEE Conference Publication No.465, pp.245-249, 1999.
  • R. Sivaramakrishna and N. S. Shashidhar, “Hu’s moment invariants: how invariant are they under skew and perspective transformations,” IEEE Conference on Communications, Power and Computing, pp.292-295, 1997.
  • S. O. Belkasim, M. Shridhar, and M. Ahmadi, “Pattern recognition with moment invariants: a comparative study and new results,” Pattern Recognition, vol. 24, pp. 1117–1138, 1991
  • Muharrem Mercimek, Kayhan, “Real object recognition using moment invariants”, S¯adhan¯a Vol. 30, Part 6, December 2005, pp. 765–775
  • Dudani, Sahibsingh “Aircraft Identification by Moment Invariants”, IEEE Conference, pp. 39-46, 2009
  • M. Gruber and K. Y. Hsu, “Moment-based image mormalization with high noise-tolerance,” Pattern Recognition, vol. 19, pp. 136–139, 1997.
  • Jan Flusser, “Moment Invariants in Image Analysis”, Proceedings of World academy of science engineering and technology, volume 11, 2006