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Braille Document Parameters Estimation for Optical Character Recognition

  • Zhenfei Tai
  • Samuel Cheng
  • Pramode Verma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5359)

Abstract

There is a significant need for a system to recognize Braille documents in order to preserve them and make them available to a larger group of visually impaired people. We introduce a high-adaptive Braille documents parameters estimation method to automatically determine the skewness, indentations, and spacing in both vertical and horizontal directions. The key element in determining the skewness of the images is based on Radon transform, which is generated from the integral of a function over straight lines, and is nicely applied in this case since Braille documents are highly directional. We demonstrate the effectiveness of skewness correction as well as the accuracy of indentation and spacing in both orientations. The proposed algorithm is an essential component of character recognition of Braille document discussed in this paper.

Keywords

Braille image Radon transform optical character recognition skewness line-spacing 

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References

  1. 1.
    Franois, G., Calders, P.: The reproduction of Braille originals by means of opticalpattern recognition. In: Proc. 5th Int. Workshop on Computer Braille Production (1985)Google Scholar
  2. 2.
    Dubus, J., Benjelloun, M., Devlaminck, V., Wauquier, F., Altmayer, P.: Image processing techniques to perform an autonomous system to translate relief Braille into black-ink, called: Lectobraille. In: Engineering in Medicine and Biology Society, 1988. Proceedings of the Annual International Conference of the IEEE, November 4-7, 1988, vol. 4, pp. 1584–1585 (1988)Google Scholar
  3. 3.
    Ritchings, R.T., Antonacopoulos, A., Drakopoulos, D.: Analysis of scanned Braille documents. In: Proc. Int. Association for Pattern Recognition Workshop on Document Analysis Systems (1994)Google Scholar
  4. 4.
    Antonacopoulos, A., Karatzas, D.: A Robust Braille Recognition System. In: Proceedings of the IAPR International Workshop on Document Analysis Systems (2004)Google Scholar
  5. 5.
    Fiddy, M.A.: The Radon transform and some of its applications. Journal of Modern Optics 32, 3–4 (1985)MathSciNetGoogle Scholar
  6. 6.
    Radon, J.: Uber die Bestimmung von Funktionen durch ihre Integralwerte langs gewisser Mannigfaltigkeiten. Berichte Sachsische Acadamie der Wissenschaften 69, 262–267 (1917)zbMATHGoogle Scholar
  7. 7.
    Beylkin, G.: Generalized Radon transform and its application. Ph.D. dissertation, New York University (1982)Google Scholar
  8. 8.
    Beylkin, G.: Inversion of the generalized Radon transform. Proc. SPIE, Inverse Optics 413, 32–40 (1983)CrossRefGoogle Scholar
  9. 9.
    Beylkin, G.: The inversion problem and applications of the generalized Radon transform. Commun. Pure Appl. Math. 37 (1984)Google Scholar
  10. 10.
    Beylkin, G.: Discrete Radon transform. IEEE Transactions on Acoustics, Speech, and Signal Processing [see also IEEE Transactions on Signal Processing] 35, 162–172 (1987)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Gonzales, R.C., Wintz, P.: Digital image processing. Addison-Wesley Longman Publishing Co., Inc., Boston (1987)Google Scholar
  12. 12.
    Strang, G.: Introduction to applied mathematics. Wellesley-Cambridge Press, Wellesley (1986)zbMATHGoogle Scholar
  13. 13.
    Ng, C., Ng, V., Lau, Y.: Regular feature extraction for recognition of Braille. In: Third International Conference on Computational Intelligence and Multimedia Applications, 1999. ICCIMA 1999. Proceedings, pp. 302–306 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Zhenfei Tai
    • 1
  • Samuel Cheng
    • 1
  • Pramode Verma
    • 1
  1. 1.School of Electrical and Computer EngineeringUniversity of OklahomaNormanUSA

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