Call for Paper - May 2023 Edition
IJCA solicits original research papers for the May 2023 Edition. Last date of manuscript submission is April 20, 2023. Read More

A Proposition of a Robust System for Historical Document Images Indexation

Print
PDF
International Journal of Computer Applications
© 2010 by IJCA Journal
Number 2 - Article 3
Year of Publication: 2010
Authors:
Nizar Zaghden
Remy Mullot
Slim Kanoun
Adel M Alimi
10.5120/1556-2076

Nizar Zaghden, Remy Mullot, Slim Kanoun and Adel M Alimi. Article:A Proposition of a Robust System for Historical Document Images Indexation. International Journal of Computer Applications 11(2):10–15, December 2010. Published By Foundation of Computer Science. BibTeX

@article{key:article,
	author = {Nizar Zaghden and Remy Mullot and Slim Kanoun and Adel M Alimi},
	title = {Article:A Proposition of a Robust System for Historical Document Images Indexation},
	journal = {International Journal of Computer Applications},
	year = {2010},
	volume = {11},
	number = {2},
	pages = {10--15},
	month = {December},
	note = {Published By Foundation of Computer Science}
}

Abstract

Characterizing noisy or ancient documents is a challenging problem up to now. Many techniques have been done in order to effectuate feature extraction and image indexation for such documents. Global approaches are in general less robust and exact than local approaches. That’s why, we propose in this paper, a hybrid system based on global approach (fractal dimension), and a local one, based on SIFT descriptor. The Scale Invariant Feature Transform seems to do well with our application since it is rotation invariant and relatively robust to changing illumination. In the first step the calculation of fractal dimension is applied to images, in order to eliminate images which have distant features than image request characteristics. Next, the SIFT is applied to show which images match well the request. However, the average matching time using the hybrid approach is better than “fractal dimension” and “SIFT descriptor” techniques, if they are used alone.

Reference

  • A.K.Bisoi, J.Mishra, "On calculation of fractal dimension of images", Pattern Recognition Letters, vol 22, N°6-7, 2001, pp 631-637.
  • B. Khelifi, N. Zaghden, A. M Alimi, R.Mullot “Unsupervised Categorization of Heterogeneous Text Images Based on Fractals “, 19th International Conference on Pattern Recognition, Tampa, Florida, USA, December 8-11, 2008.
  • S.S.Chen, J.M.Keller and R.M.Crownover, "On the calculation of fractal features from images", IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol 15, N°1, 1993, pp 1087-1090.
  • D. Lowe, “Distinctive image features from scale-invariant keypoints”. Intl. J. of Comp. Vision 60 (2004), pp 91–110.
  • M.A. Maatar, A.R. Honson, E.L. Miller,”Sign Classification for the Visually Impaired”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03, pp 26-36.
  • C. Schmid and R. Mohr, “Local grayvalue invariants for image retrieval”, lEEE Trans. I’attem Analysis and Machine Intelligence, vo1.19, 110.5, pp.530-535, 1997.
  • N.Sarkar and B.B.Chaudhuri, " An efficient differential box-counting approach to compute fractal dimension of image", IEEE Trans.Systems, man, and cybernitics, vol 24, N° 1, January 1994, pp 115-120.
  • N. Zaghden, B.Khelifi, A.M Alimi, R.Mullot, “Text Recognition in both ancient and cartographic documents”, VSMM 2008, pp 98-101.
  • N.Zaghden,S.B.Moussa, A.M. Alimi, "Reconnaissance des fontes arabes par l’utilisation des dimensions fractales et des ondelettes", Colloque International Francophone sur l’Ecrit et le Document (CIFED 06),Fribourg(Suisse) September 18-21,2006,pp-277_282.
  • J. Zhang, M. Marsza_lek , “Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study", International Journal of Computer Vision, Vol.73, No. 2,2007, pp. 213-238.