Segmentation et classification des zones d'une page de document
Identifieur interne : 001C07 ( Main/Exploration ); précédent : 001C06; suivant : 001C08Segmentation et classification des zones d'une page de document
Auteurs : Jean-Marc Vauthier [France] ; Abdel Belaïd [France]Source :
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Abstract
This paper proposes a methodology for complex document segmentation based on textual content and shape. The textual content corresponds with printed text and it is verified by text-word analysis using dictionary and regular expressions variable that are adapted to noise. This allows knowing where the interested expressions are placed (address, phone number etc.) The non-textual content is segmented in zone considering size and distance between connected components in order to classify zones like logo, signature, and table. To make that, features are extracted like run length, Bi level Co-occurrence... This classification is based on a modified boosting method and decision trees. The modification is about the calculation of the probability to draw training data. Compare to OCRs that are able to classify text, tables and pictures, our methodology increases the performance and allows the detection of other zones like handwritten text, logo, signature, table and tampon.
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The textual content corresponds with printed text and it is verified by text-word analysis using dictionary and regular expressions variable that are adapted to noise. This allows knowing where the interested expressions are placed (address, phone number etc.) The non-textual content is segmented in zone considering size and distance between connected components in order to classify zones like logo, signature, and table. To make that, features are extracted like run length, Bi level Co-occurrence... This classification is based on a modified boosting method and decision trees. The modification is about the calculation of the probability to draw training data. Compare to OCRs that are able to classify text, tables and pictures, our methodology increases the performance and allows the detection of other zones like handwritten text, logo, signature, table and tampon.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Metz</li><li>Nancy</li></settlement><orgName><li>Université de Lorraine</li></orgName></list><tree><country name="France"><region name="Grand Est"><name sortKey="Vauthier, Jean Marc" sort="Vauthier, Jean Marc" uniqKey="Vauthier J" first="Jean-Marc" last="Vauthier">Jean-Marc Vauthier</name></region><name sortKey="Belaid, Abdel" sort="Belaid, Abdel" uniqKey="Belaid A" first="Abdel" last="Belaïd">Abdel Belaïd</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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