Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Character Matching Using a Hausdorff Distance

Hausdorff 거리를 이용한 문자 매칭

  • Kim, Kyeongtaek (Department of Industrial and Management Engineering, Hannam University) ;
  • Kyung, Ji Hun (Department of Industrial and Management Engineering, Hannam University)
  • 김경택 (한남대학교 산업경영공학과) ;
  • 경지훈 (한남대학교 산업경영공학과)
  • Received : 2015.02.16
  • Accepted : 2015.04.16
  • Published : 2015.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

The Hausdorff distance is commonly used as a similarity measure between two-dimensional binary images. Since the document images may be contaminated by a variety of noise sources during transmission, scanning or conversion to digital form, the measure should be robust to the noise. Original Hausdorff distance has been known to be sensitive to outliers. Transforming the given image to grayscale image is one of methods to deal with the noises. In this paper, we propose a Hausdorff distance applied to grayscale images. The proposed method is tested with synthetic images with various levels of noises and compared with other methods to show its robustness.

Keywords

References

  1. Baudrier, E., Nicolier, F., Millon, G., and Ruan, S., Binary-Image Comparison with Local-Dissimilarity Quantification. Pattern Recognition, 2008, Vol. 42, pp. 1461-1478.
  2. Choudhary, A., Rishi, R., and Ahlawat, S., A New Approach to Detect and Extract Characters from Off-Line Printed Images and Text. Procedia Computer Science, 2013, Vol. 17, pp. 434-440. https://doi.org/10.1016/j.procs.2013.05.056
  3. Dubuission, M.P. and Jain, A.K., A Modified Hausdorff Distance for Object Matching. Proc. of the 12th International Conference on Pattern Matching, 1994, Vol. 1, pp. 566-568.
  4. Farahmand, A., Sarrafzadeh, A., and Shanbehzadeh, J., Document Image Noises and Removal Methods. Proceedings of the International Multi Conference of Engineering and Computer Science, 2013, Vol. 1, pp. 436-440.
  5. Hutternlocher, D.P., Klanderman, G.A., and Rucklidge, W.J., "Comparing Images Using the Hausdorff Distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993, Vol. 15, No. 9, pp. 850-863. https://doi.org/10.1109/34.232073
  6. Kwon, O.-K., Sim, D.-G., and Park, R.-H., Robust Hausdorff Distance Matching Algorithms using Pyramidal Structures. Pattern Recognition, 2001, Vol. 34, No. 10, pp. 2005-2013. https://doi.org/10.1016/S0031-3203(00)00132-1
  7. Lu, Y., Tan, C.L., Huang, W., and Fan, L., An Approach to Word Image Matching based on Weighted Hausdorff Distance. Proc. of the 6th International Conference on Document Analysis and Recognition, 2001, pp. 921-925.
  8. Paumard, J., Robust Comparison of Binary Images. Pattern Recognition Letters, 1997, Vol. 18, No. 10, pp. 1057-1063. https://doi.org/10.1016/S0167-8655(97)80002-5
  9. Premchaiswadi, N., Yimngam, S., and Premchaiswadi, W., A Scheme for Salt and Pepper Noise Reduction on Graylevel and Color Image. Proceedings of the 9th WSEAS International Conference on Signal Processing, Computational Geometry and Artificial Vision, 2009, pp. 57-61.
  10. Sim, D.-G., Kwon, O.-K., and Park, R.-H., Object Matching Algorithms using Robust Hausdorff Distance Measures. IEEE Trans. on Image Processing, 1999, Vol. 8, No. 3, pp. 425-429. https://doi.org/10.1109/83.748897
  11. Takacs, B., Comparing Faces using the Modified Hausdorff Distance. Pattern Recognition, 1998, Vol. 31, No. 12, pp. 1873-1881. https://doi.org/10.1016/S0031-3203(98)00076-4
  12. Zhao, C., Shi, W., and Deng, Y., A New Hausdorff Distance for Image Matching. Pattern Recognition Letters, 2005, Vol. 26, pp. 581-586. https://doi.org/10.1016/j.patrec.2004.09.022