We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Reseach Article

Hindi Character Segmentation in Document Images using Level set Methods and Non-linear Diffusion

by Manjusha K, Sachin Kumar S, Jolly Rajendran, K. P. Soman
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 44 - Number 16
Year of Publication: 2012
Authors: Manjusha K, Sachin Kumar S, Jolly Rajendran, K. P. Soman
10.5120/6351-8745

Manjusha K, Sachin Kumar S, Jolly Rajendran, K. P. Soman . Hindi Character Segmentation in Document Images using Level set Methods and Non-linear Diffusion. International Journal of Computer Applications. 44, 16 ( April 2012), 42-49. DOI=10.5120/6351-8745

@article{ 10.5120/6351-8745,
author = { Manjusha K, Sachin Kumar S, Jolly Rajendran, K. P. Soman },
title = { Hindi Character Segmentation in Document Images using Level set Methods and Non-linear Diffusion },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 44 },
number = { 16 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 42-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume44/number16/6351-8745/ },
doi = { 10.5120/6351-8745 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:35:46.125368+05:30
%A Manjusha K
%A Sachin Kumar S
%A Jolly Rajendran
%A K. P. Soman
%T Hindi Character Segmentation in Document Images using Level set Methods and Non-linear Diffusion
%J International Journal of Computer Applications
%@ 0975-8887
%V 44
%N 16
%P 42-49
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Hindi is the national language of India, spoken by more than 500 million people and is the second most popular spoken language in the world, after Chinese. Digital document imaging is gaining popularity for application to serve at libraries, government offices, banks etc. In this paper, we intend to provide a study on character binarization and segmentation of Hindi document images, which are the essential pre-processing steps in several applications like digitization of historically relevant books. In the case of historical documents, the document image may have stains, may not be readable, the background could be non-uniform and may be faded because of aging. In those cases the task of binarization and segmentation becomes challenging, and it affects the overall accuracy of the system. So these processes should be carried out accurately and efficiently. Here we experiment level set method in combination with diffusion techniques for improving the accuracy of segmentation in document process task.

References
  1. P. Perona and J. Malik, "Scale-Space and Edge Detection using Anisotropic Diffusion", IEEE Trans. Pattern analysis and Machine Intelligence, vol. 12, no. 7, pp. 629639, July 1990
  2. F. Drira, F. LeBourgeosis, H. Emptoz, "A new PDE-based approach for singularitypreserving regularization: application to degraded characters restoration", International Journal on Document Analysis and Recognition (IJDAR)(2011): 1-30, May 2011
  3. C. Xu, A. YezziJr, J. L. Prince, "On the relationship between parametric and geometric active contours", Asilmor Conf. Signals, Systems, and Computers, pp. 483-489, 2000
  4. Kaihua Zhang, Lei Zhang, Huihui Song, Wengang Zhou, "Active contour with selective local or global segmentation: A new formulation and level set method", Elsever, 2009
  5. Xavier Bresson, "A Short Guide on a Fast Global Minimization Algorithm for Active Contour Models", April 22, 2009.
  6. S. K. Weeratunga, C. Kamath, "An Investigation of Implicit active contours for scientific image segmentation", in: Visual Communications and Image Processing Conference, 2003
  7. M. Kass, A. Witkin, D. Terzopoulos, "Snakes: active contour models", International Journal of Computer Vision 1 (1988) 321–331.
  8. N. Xu, N. Ahuia, R. Bansal, "Object segmentation using graph cuts based active contours", Computer Vision and Image Understanding 107 (2007) 210–224
  9. V. Caselles, R. Kimmel, G. Sapiro, Geodesic active contours, in: Processing of IEEE International Conference on Computer Vision'95, Boston, MA, 1995, pp. 694–699
  10. M. Ben Salah, A. Mitiche and I. Ben Ayed, "Effective Level Set Image Segmentation with a Kernel Induced Data Term", IEEE Transactions on Image processing, vol. 19, no 1, pp. 220–232, 2010.
  11. T. Chan, L. Vese, "Active contours without edges", IEEE Transaction on Image Processing 10 (2) (2001) 266–277
  12. G. P. Zhu, Sh. Q. Zhang, Q. SH. Zeng, Ch. H. Wang, "Boundary-based image Segmentation"
  13. J. Ohya, A. Shio, and S. Akamatsu, "Recognizing characters in scene images", IEEE Trans. Pattern Anal. Mach. Intell. , 16(2), 1994, pp. 214–220
  14. Y. Zhong, K. Karu, and A. K. Jain, "Locating text in complex color images", Pattern Recognition, 28(10) , 1995, pp. 1523–1535
  15. O. D. Trier and T. Taxt, "Evaluation of binarization methods for document images", IEEE Trans. Pattern Anal. Machine Intell. , vol. 17, Mar. 1995, pp. 312-315.
  16. . T. Abak, U. Baris, and B. Sankur, "The Performance Evaluation of Thresholding Algorithms for Optical CharacterRecognition", ICDAR 97, Ulm, Germany, 1997, pp. 697-700
  17. Mehmet Sezgin, "Survey over image thresholding techniques and quantitative performance evaluation", Journal of electronic imaging, 13, 146,2004, doi:10. 1117/1. 1631315
  18. R. Malladi, R. Kimmel, D. Adalsteinsson, G. Sapiro, V. Caselles, and J. A. Sethian. "A geometric approach to segmentation and analysis of 3d medical images", In MMBIA '96: Proceedings of the 1996Workshop on Mathematical Methods in Biomedical Image Analysis(MMBIA '96), page 244, Washington, DC, USA, 1996. IEEE Computer Society
  19. C. V. Jawahar, M. N. S. S. K. Pavan Kumar, S. S. Ravi Kiran, "A Bilingual OCR for Hindi-Telugu Documents and its Applications", ICDAR, vol. 1, pp. 408, Seventh International Conference on Document Analysis and Recognition (ICDAR'03) - Volume 1, 2003
  20. U. Pal, B. B. Chaudhuri, "Indian Script Character Recognition: A survey", Patter Recognition, vol. 37, pp. 1887-1889, 2004
  21. E. Nadernejad, H. Koohi, and H. Hassanpour, "PDEs-Based Method for Image Enhancement," Applied Mathematical Sciences, Vol. 2, No. 20, pp. 981 – 993, 2008
  22. H. Philips, The Level Set Method, http://web. mit. edu/aram/www/work/thesis. pdf
  23. K. Kang, C. Weinberger, W. Cai, "A Short Essay on Variational Calculu"s, Dept of Mechanical Stanford University, May 2006
  24. Hyunwoo Kim, Jeong-Hun Jang, Ki-Sang Hong, "Edge-Enhancing Super-Resolution Using Anisotropic Diffusion", ©2001 IEEE
  25. PavelMr´azek, "Nonlinear Diffusion for ImageFiltering and Monotonicity Enhancement"
  26. Joachim Weickert,, "Coherence-Enhancing Diffusion Filtering", International Journal of Computer Vision 1999 Kluwer Academic Publishers.
  27. Perona, P. and Malik, J. "Scale space and edge detection using anisotropic diffusion", IEEE Trans. Pattern Anal. Mach. Intell. ,Vol. 12, pp. 629–639
Index Terms

Computer Science
Information Sciences

Keywords

Level Set Method Binarization Segmentation Convex Optimization.