Complex documents images segmentation based on steerable pyramid features
Identifieur interne : 000135 ( France/Analysis ); précédent : 000134; suivant : 000136Complex documents images segmentation based on steerable pyramid features
Auteurs : Mohamed Benjelil [Tunisie] ; Slim Kanoun [Tunisie] ; Rémy Mullot [France] ; Adel Alimi [Tunisie]Source :
English descriptors
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Abstract
Page segmentation and classification is very important in document layout analysis system before it is presented to an OCR system or for any other subsequent processing steps. In this paper, we propose an accurate and suitably designed system for complex documents segmentation. This system is based on steerable pyramid transform. The features extracted from pyramid sub-bands serve to locate and classify regions into text (either machine-printed or handwritten) and non-text (images, graphics, drawings or paintings) in some noise-infected, deformed, multilingual, multi-script document images. These documents contain tabular structures, logos, stamps, handwritten script blocks, photographs, etc. The encouraging and promising results obtained on 1,000 official complex document images data set are presented in this research paper. We compared our results with those from existing state-of-the-art methods. This comparison shows that the proposed method performs consistently well on large sets of complex document images.
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DOI: 10.1007/s10032-010-0113-9
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Hal:hal-00495728Le document en format XML
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The features extracted from pyramid sub-bands serve to locate and classify regions into text (either machine-printed or handwritten) and non-text (images, graphics, drawings or paintings) in some noise-infected, deformed, multilingual, multi-script document images. These documents contain tabular structures, logos, stamps, handwritten script blocks, photographs, etc. The encouraging and promising results obtained on 1,000 official complex document images data set are presented in this research paper. We compared our results with those from existing state-of-the-art methods. This comparison shows that the proposed method performs consistently well on large sets of complex document images.</div></front></TEI><affiliations><list><country><li>France</li><li>Tunisie</li></country><region><li>Poitou-Charentes</li></region><settlement><li>La Rochelle</li></settlement><orgName><li>Université de La Rochelle</li></orgName></list><tree><country name="Tunisie"><noRegion><name sortKey="Benjelil, Mohamed" sort="Benjelil, Mohamed" uniqKey="Benjelil M" first="Mohamed" last="Benjelil">Mohamed Benjelil</name></noRegion><name sortKey="Alimi, Adel" sort="Alimi, Adel" uniqKey="Alimi A" first="Adel" last="Alimi">Adel Alimi</name><name sortKey="Kanoun, Slim" sort="Kanoun, Slim" uniqKey="Kanoun S" first="Slim" last="Kanoun">Slim Kanoun</name></country><country name="France"><region name="Poitou-Charentes"><name sortKey="Mullot, Remy" sort="Mullot, Remy" uniqKey="Mullot R" first="Rémy" last="Mullot">Rémy Mullot</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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