Extraction of Homogeneous Regions in Historical Document Images

Maroua Mehri, Pierre Héroux, Nabil Sliti, Petra Gomez-Krämer, Najoua Essoukri Ben Amara, Rémy Mullot

2015

Abstract

To reach the objective of ensuring the indexing and retrieval of digitized resources and offering a structured access to large sets of cultural heritage documents, a raising interest to historical document image segmentation has been generated. In fact, there is a real need for automatic algorithms ensuring the identification of homogenous regions or similar groups of pixels sharing some visual characteristics from historical documents (i.e. distinguishing graphic types, segmenting graphical regions from textual ones, and discriminating text in a variety of situations of different fonts and scales). Indeed, determining graphic regions can help to segment and analyze the graphical part in historical heritage, while finding text zones can be used as a pre-processing stage for character recognition, text line extraction, handwriting recognition, etc. Thus, we propose in this article an automatic segmentation method for historical document images based on extraction of homogeneous or similar content regions. The proposed algorithm is based on using simple linear iterative clustering (SLIC) superpixels, Gabor filters, multi-scale analysis, majority voting technique, connected component analysis, color layer separation, and an adaptive run-length smoothing algorithm (ARLSA). It has been evaluated on 1000 pages of historical documents and achieved interesting results.

References

  1. Achanta, R., Shaji, A., Lucchi, A., Fua, P., and Sü sstrunk, S. (2012). SLIC superpixels compared to state-of-theart superpixel methods. PAMI, pages 2274-2282.
  2. Brunessaux, S., Giroux, P., Grilheres, B., Manta, M., Bodin, M., Choukri, K., Galibert, O., and Kahn, J. (2014). The Maurdor project: Improving automatic processing of digital documents. In DAS, pages 349-354. IEEE.
  3. Chang, T. and Kuo, C. C. J. (1992). Texture segmentation with tree-structured wavelet transform. In TFTSA, pages 543-546.
  4. Coustaty, M., Raveaux, R., and Ogier, J. M. (2011). Historical document analysis: A review of French projects and open issues. In EUSIPCO, pages 1445-1449.
  5. Gabor, D. (1946). Theory of communication. Part 1: The analysis of information. Journal of the Institution of Electrical Engineers - Part III: Radio and Communication Engineering, pages 429-441.
  6. Lam, L. and Suen, C. Y. (1997). Application of majority voting to pattern recognition: an analysis of its behavior and performance. SMC, pages 553-568.
  7. Li, J., Wang, J. Z., and Wiederhold, G. (2000). Classification of textured and non-textured images using region segmentation. IP, pages 754-757.
  8. Liu, M. Y., Tuzel, O., Ramalingam, S., and Chellappa, R. (2011). Entropy rate superpixel segmentation. In CVPR, pages 2097-2104.
  9. MacQueen, J. B. (1967). Some methods for classification and analysis of multivariate observations. In Berkeley Symposium on Mathematical Statistics and Probability, pages 281-297. University of California Press.
  10. Mehri, M., Gomez-Krämer, P., Héroux, P., Boucher, A., and Mullot, R. (2013). Texture feature evaluation for segmentation of historical document images. In HIP, pages 102-109.
  11. Okun, O. and Pietikäinen, M. (1999). A survey of texturebased methods for document layout analysis. In WTAMV, pages 137-148.
  12. Otsu, N. (1979). A threshold selection method from graylevel histograms. SMC, pages 62-66.
  13. Rosenfeld, A. and Pfaltz, J. L. (1966). Sequential operations in digital picture processing. Journal of the ACM, pages 471-494.
  14. Wahl, F. M., Wong, K. Y., and Casey, R. G. (1982). Block segmentation and text extraction in mixed text/image documents. CGIP, pages 375-390.
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Paper Citation


in Harvard Style

Mehri M., Héroux P., Sliti N., Gomez-Krämer P., Ben Amara N. and Mullot R. (2015). Extraction of Homogeneous Regions in Historical Document Images . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-091-8, pages 47-54. DOI: 10.5220/0005265500470054


in Bibtex Style

@conference{visapp15,
author={Maroua Mehri and Pierre Héroux and Nabil Sliti and Petra Gomez-Krämer and Najoua Essoukri Ben Amara and Rémy Mullot},
title={Extraction of Homogeneous Regions in Historical Document Images},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={47-54},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005265500470054},
isbn={978-989-758-091-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)
TI - Extraction of Homogeneous Regions in Historical Document Images
SN - 978-989-758-091-8
AU - Mehri M.
AU - Héroux P.
AU - Sliti N.
AU - Gomez-Krämer P.
AU - Ben Amara N.
AU - Mullot R.
PY - 2015
SP - 47
EP - 54
DO - 10.5220/0005265500470054