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A novel triangulation procedure for thinning cursive text

  • Session T3A: OCR and Applications
  • Conference paper
  • First Online:
Computer Vision — ACCV'98 (ACCV 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1351))

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Abstract

Thinning or skeletonization can contribute tremendously to efficient feature extraction and classification. This paper describes a novel thinning procedure which decomposes a polygonal approximation of the inner and outer boundaries of a word image into a set of triangles. These triangles are classified into three types and replaced either by three lines at branches or by single lines to form a completely connected skeleton which is one pixel wide. The procedure also automatically generates a graph representation of the skeleton for use by subsequent analysis steps. The procedure has been tested on a variety of Arabic and English handwritten words and found to produce very few spurious features. The speed of this method does not depend strongly on the resolution of the digital image unlike commonly used thinning methods which iteratively remove layers from the boundary of words. In addition, it can be used for thinning any line-like patterns such as fingerprints, chromosomes and line drawings.

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References

  1. Amin A., Al-Sadoun H., and Fischer S. Hand-printed Arabic character recognition system using an artificial network, Pattern Recognition 29(4), 663–675 (1996).

    Google Scholar 

  2. Fitz A. and Green R. Fingerprint pre-processing on a hexagonal grid, Proceedings of the 1995 European Convention on Security and Detection, pp. 257–260, Brighton, UK (May 1995).

    Google Scholar 

  3. DiezHiguera J., DiazPernas F. and LopezCoronado J. Neural network architecture for automatic chromosome analysis, Proceedings of SPIE — Applications of Artificial Neural Networks in Image Processing, pp. 85–94, CA, USA (February 1996).

    Google Scholar 

  4. Ye Q. and Danielsson P. Inspection of printed circuits boards by connectivity preserving shrinking, IEEE Trans. Pattern Anal. and Mach. Intell. 10(5), 737–743 (1988).

    Google Scholar 

  5. Blum H. “A transformation for extracting new descriptors of shape” in Models for the perception of speech and visual fonn, Wathen-Dune, W., ed., MIT Press, Cambridge, Mass (1967).

    Google Scholar 

  6. Lam L., Lee S., and Suen C.Y. Thinning methodologies-a comprehensive Survey, IEEE Trans. Pattern Anal. and Mach. Intell. 14, 869–885 (1992).

    Google Scholar 

  7. Datta A. and Parui S. A robust parallel thinning algorithm for binary images, Pattern Recognition 27 (9), 1181–1192 (1994).

    Google Scholar 

  8. Altwaijri M., Ayoubi R. and Bayoumi M. Skeletonization of Arabic characters using a neural network mapped on Maspar, World Congress on Neural Networks-International Neural Network Society Annual Meeting, 2, pp. 653–658 (1994).

    Google Scholar 

  9. Niblack C.W., Gibbons P.B. and Capson D.W. Generating skeletons and centerlines from the distance transform, CVGIP: Graphical Models and Image Processing 54 (5), 420–437 (1992).

    Google Scholar 

  10. Shih F.Y. and Pu C.C. A skeletonization algorithm by maxima tracking on Euclidean distance transform, Pattern Recognition 28 (3), 331–341 (1995).

    Google Scholar 

  11. Tarjan R. and Wyk V. An O(n log log n)-time algorithm for triangulating a simple polygon, SIAM J. Comput. 17,143–178 (Feb. 1988).

    Google Scholar 

  12. Yuan J. and Suen C.Y. An optimal O(n) algorithm for identifying line segments from a sequence of chain codes, Pattern Recognition 28 (5), 635–646 (1995).

    Google Scholar 

  13. O'Rourke J. “Computational geometry in C”, Cambridge University Press.

    Google Scholar 

  14. Melhi M.H., Ipson S.S. and Booth W. A database for research in Arabic handwriting recognition, Dept. of E & EE, Internal Report 595, University of Bradford, UK (1997)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electronic and Electrical Engineering, University of Bradford, BD7 1 DP, Bradford, UK

    Stanley S. Ipson, Muhammed Melhi & William Booth

Authors
  1. Stanley S. Ipson
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  2. Muhammed Melhi
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  3. William Booth
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Editor information

Roland Chin Ting-Chuen Pong

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© 1997 Springer-Verlag Berlin Heidelberg

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Ipson, S.S., Melhi, M., Booth, W. (1997). A novel triangulation procedure for thinning cursive text. In: Chin, R., Pong, TC. (eds) Computer Vision — ACCV'98. ACCV 1998. Lecture Notes in Computer Science, vol 1351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63930-6_119

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  • DOI: https://doi.org/10.1007/3-540-63930-6_119

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63930-5

  • Online ISBN: 978-3-540-69669-8

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