Improving the use of contours and skeletons for off-line cursive script segmentation

  • A. Chianese
  • M. De Santo
  • A. Picariello
Poster Session D: Biomedical Applications, Detection, Control & Surveillance, Inspection, Optical Character Recognition
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1311)

Abstract

Segmentation process represents a very difficult task for handwritten recognition aims, due to large variations involved in cursive scripts. This paper describes a method for off-line unconstrained handwritten segmentation, based on a combined use of contour and skeleton derived information. Experiments are discussed and successful segmentation results are reported.

Keywords

Text Line Validation Phase Character Segmentation Segmentation Point Lower Contour 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. [1]
    C. C. Tappert, C.Y. Suen, T. Wakahara, “The State of the art in on-line handwriting recognition”, IEEE PAMI 12 (8):787–808, 1990.Google Scholar
  2. [2]
    J-C Simon, “Off-line cursive word recognition”, Proceedings of the IEEE, 80(7):1150–1161, July 1992CrossRefGoogle Scholar
  3. [3]
    C. Y. Suen, R. Legault” C. Nadal, M. Cheriet, L. Lam, “dBuilding a new generation of handwriting recognition systems”, Pattern Recognition Letters, pp. 303–315, 1993Google Scholar
  4. [4]
    Y. Lu and M. Shridar, “Character Segmentation in Handwritten words — an overview”, Pattern Recognition, vol. 29, n. 1, 1996, pp. 77–96CrossRefGoogle Scholar
  5. [5]
    M. Maier, “Separating characters in Scripted Documents” Proc. of ICPR 1986, Paris, 1986Google Scholar
  6. [6]
    E. Lecolinet, J.P. Crettex, “A Grapheme Based Segmentation Technique for Cursive Script Recognition”, Proc. of 1. ICDAR 91,: 740–748, Saint Malo, France, 1991Google Scholar
  7. [7]
    S. Kahan, T. Pavlidis and H. S. Baird, “On the recognition of printed characters of any font and size”, IEEE Trans. Pattern Analysis and Machine Intelligence, PAMI-9(2), pp. 274–287, (1987).Google Scholar
  8. [8]
    G. Boccignone, A. Chianese, M. De Santo and A. Picariello, “Improving the use of Contours for Off-Line Cursive Script Segmentation”, Lecture Notes in Computer Science, vol. 974, 1995, pp. 545–550Google Scholar
  9. [9]
    R. M. Bozinovic, S. N. Srihari, “Off-line cursive word recognition”, IEEE PAMI, 11(1): 68–83, January 1989Google Scholar
  10. [10]
    S. N. Srihari, R. M. Bozinovic, “A multi-level perception approach to reading cursive script”, Artificial Intelligence, 33:217–255, 1987CrossRefGoogle Scholar
  11. [11]
    B. A. Yanikoglu, P. A. Sandon, “Recognizing Off-line Cursive Handriting”, proc. of ICDAR'93, pp. 397–403Google Scholar
  12. [12]
    G. Boccignone, A Chianese, L. P. Cordella and A. Marcelli, “Recovering dynamic information from static handwriting”, Pattern Recognition, Vol. 26, n. 3, Pattern Recognition, Vol. pp, 409-418Google Scholar
  13. [13]
    D. S. Doermann and A. Rosenfeld, “Recovery of temporal Information from static Images of Handwriting”, Proc. 1992 IEEE Comp. Soc. Conf. On Computer Vision and Pattern Recognition, Urbana Champaign, Illinois, pp. 162–168.Google Scholar
  14. [14]
    C. Arcelli and G. Sanniti di Baja, “A Thinning algorithm based on prominence detection”, Pattern Recognition 13, 1981, pp. 225–235CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • A. Chianese
    • 1
  • M. De Santo
    • 2
  • A. Picariello
    • 1
  1. 1.DIS Università Degli Studi di Napoli ‘Federico II’NapoliItaly
  2. 2.DIIIE Università Degli Studi di SalernoFisciano (Sa)Italy

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