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Conference papers

Texture feature evaluation for segmentation of historical document images

Abstract : Texture feature analysis has undergone tremendous growth in recent years. It plays an important role for the analysis of many kinds of images. More recently, the use of texture analysis techniques for historical document image segmen-tation has become a logical and relevant choice in the conditions of significant document image degradation and in the context of lacking information on the document structure such as the document model and the typographical parameters. However, previous work in the use of texture analysis for segmentation of digitized historical document images has been limited to separately test one of the well-known texture-based approaches such as autocorrelation function, Grey Level Co-occurrence Matrix (GLCM), Gabor filters, gradient, wavelets, etc. In this paper we raise the question of which texture-based method could be better suited for discriminating on the one hand graphical regions from textual ones and on the other hand for separating textual regions with different sizes and fonts. The objective of this paper is to compare some of the well-known texture-based approaches: autocorrelation function, GLCM, and Gabor filters , used in a segmentation of digitized historical document images. Texture features are briefly described and quantitative results are obtained on simplified historical document images. The achieved results are very encouraging.
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Contributor : Maroua Mehri Connect in order to contact the contributor
Submitted on : Wednesday, December 2, 2015 - 10:55:12 PM
Last modification on : Thursday, May 12, 2022 - 3:39:21 PM
Long-term archiving on: : Thursday, March 3, 2016 - 3:01:46 PM


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Maroua Mehri, Petra Gomez-Krämer, Pierre Héroux, Alain Boucher, Rémy Mullot. Texture feature evaluation for segmentation of historical document images. International Workshop on Historical Document Imaging and Processing (HIP), Aug 2013, Washington, DC, United States. pp.102-109, ⟨10.1145/2501115.2501121⟩. ⟨hal-01237230⟩



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