Journal of Computing Science and Engineering

Korean Institute of Information Scientists and Engineers (한국정보과학회)
권호 표지
DOI QR코드

DOI QR Code

Stroke Width Based Skeletonization for Text Images

  • Nguyen, Minh Hieu (Dept. of Electronics and Computer Eng., Chonnam National University) ;
  • Kim, Soo-Hyung (Dept. of Electronics and Computer Eng., Chonnam National University) ;
  • Yang, Hyung Jeong (Dept. of Electronics and Computer Eng., Chonnam National University) ;
  • Lee, Guee Sang (Dept. of Electronics and Computer Eng., Chonnam National University)
  • Received : 2014.06.12
  • Accepted : 2014.07.29
  • Published : 2014.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Skeletonization is a morphological operation that transforms an original object into a subset, which is called a 'skeleton'. Skeletonization has been intensively studied for decades and is a challenging issue especially for special target objects. This paper proposes a novel approach to the skeletonization of text images based on stroke width detection. First, the preliminary skeleton is detected by using a Canny edge detector with a Tensor Voting framework. Second, the preliminary skeleton is smoothed, and junction points are connected by interpolation compensation. Experimental results show the validity of the proposed approach.

Keywords

References

  1. P. Morrison and J. J. Zou, "An effective skeletonization method based on adaptive selection of contour points," in Proceedings of the 3rd International Conference on Information Technology and Application, Sydney, Australia, 2005, pp. 644-649.
  2. T. Ju, M. L. Baker, and W. Chiu, "Computing a family of skeletons of volumetric models for shape description," Computer-Aided Design, vol. 39, no. 5, pp. 352-360, 2007. https://doi.org/10.1016/j.cad.2007.02.006
  3. G. Nemeth and K. Palagyi, "Topology preserving parallel thinning algorithms," International Journal of Imaging Systems and Technology, vol. 21, no. 1, pp. 37-44, 2011. https://doi.org/10.1002/ima.20272
  4. K. Saeed, M. Tabedzki, M. Rybnik, and M. Adamski, "K3M: a universal algorithm for image skeletonization and a review of thinning techniques," International Journal of Applied Mathematics and Computer Science, vol. 20, no. 2, pp. 317-335, 2010.
  5. W. R. Quadros, K. Shimada, and S. J. Owen, "Skeletonbased computational method for the generation of a 3D finite element mesh sizing function," Engineering with Computers, vol. 20, no. 3, pp. 249-264, 2004. https://doi.org/10.1007/s00366-004-0292-4
  6. Z. Guo and R. W. Hall, "Fast fully parallel thinning algorithms," CVGIP: image understanding, vol. 55, no. 3, pp. 317-328, 1992. https://doi.org/10.1016/1049-9660(92)90029-3
  7. M. Ahmed and R. Ward, "A rotation invariant rule-based thinning algorithm for character recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 12, pp. 1672-1678, 2002. https://doi.org/10.1109/TPAMI.2002.1114862
  8. Y. Y. Zhang and P. S. P. Wang, "A parallel thinning algorithm with two-subiteration that generates one-pixel-wide skeletons," in Proceedings of the 13th International Conference on Pattern Recognition, Vienna, Austria, 1996, pp. 457-461.
  9. J. Canny, "A computational approach to edge detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679-698, 1986.
  10. G. Medioni, M. S. Lee, and C. K. Tang, A Computational Framework for Segmentation and Grouping, Amsterdam: Elsevier, 2000.
  11. B. Epshtein, E. Ofek, and Y. Wexler, "Detecting text in natural scenes with stroke width transform," in Proceedings of the 23rd IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, 2010, pp. 2963-2970.
  12. X. You and Y. Y. Tang, "Wavelet-based approach to character skeleton," IEEE Transactions on Image Processing, vol. 16, no. 5, pp. 1220-1231, 2007. https://doi.org/10.1109/TIP.2007.891800
  13. C. Harris and M. Stephens, "A combined corner and edge detector," in Proceedings of the 4th Alvey Vision Conference, 1988, pp 147-151.
  14. H. Blum, "A transformation for extracting new descriptors of shape," in Proceedings of a Symposium on Models for the Perception of Speech and Visual Form, 1967, pp. 362-380.
  15. R. M. Haralick and L. G. Shapiro, Computer and Robot Vision (Volume 1), Reading: Addison-Wesley, 1991.
  16. R. Ogniewicz and M. Ilg, "Voronoi skeletons: theory and applications," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Champaign, IL, 1992, pp. 63-69.