Robustness Assessment of Texture Features for the Segmentation of Ancient Documents

Abstract : For the segmentation of ancient digitized document images, it has been shown that texture feature analysis is a consis-tent choice for meeting the need to segment a page layout under significant and various degradations. In addition, it has been proven that the texture-based approaches work effectively without hypothesis on the document structure, neither on the document model nor the typographical parameters. Thus, by investigating the use of texture as a tool for automatically segmenting images, we propose to search homogeneous and similar content regions by analyzing texture features based on a multiresolution analysis. The preliminary results show the effectiveness of the texture features extracted from the autocorrelation function, the Grey Level Co-occurrence Matrix (GLCM), and the Gabor filters. In order to assess the robustness of the proposed texture-based approaches, images under numerous degradation models are generated and two image enhancement algorithms (non-local means filtering and superpixel techniques) are evaluated by several accuracy metrics. This study shows the robustness of texture feature extraction for segmentation in the case of noise and the uselessness of a denoising step.
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Communication dans un congrès
IAPR International Workshop on Document Analysis Systems, Apr 2014, Tours, France. pp.293 - 297, 2014, Proceedings of the IAPR International Workshop on Document Analysis Systems. <10.1109/DAS.2014.22>
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Dernière modification le : jeudi 9 février 2017 - 16:58:52
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Maroua Mehri, Van Cuong Kieu, Mohamed Mhiri, Pierre Héroux, Petra Gomez-Krämer, et al.. Robustness Assessment of Texture Features for the Segmentation of Ancient Documents. IAPR International Workshop on Document Analysis Systems, Apr 2014, Tours, France. pp.293 - 297, 2014, Proceedings of the IAPR International Workshop on Document Analysis Systems. <10.1109/DAS.2014.22>. <hal-01119156>

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