Extraction de structure de documents manuscrits non contraints par Champs Aléatoires Conditionnels 2D
Identifieur interne : 000A83 ( Main/Exploration ); précédent : 000A82; suivant : 000A84Extraction de structure de documents manuscrits non contraints par Champs Aléatoires Conditionnels 2D
Auteurs : Florent Montreuil [France] ; Emmanuèle Grosicki [France] ; Stéphane Nicolas [France] ; Laurent Heutte [France]Source :
Descripteurs français
- mix :
Abstract
Cette article décrit une nouvelle approche utilisant des Champs Aléatoires Conditionnels (CACs) pour extraire la mise en page de documents manuscrits non contraints. Dans cette approche, l'extraction de la mise en page est considérée comme une tâche d'étiquetage consistant à assigner une étiquette à chaque pixel de l'image du document. Le modèle CAC donne directement accès à la probabilité conditionnelle globale d'un étiquetage de l'image sachant des caractéristiques image et des connaissances a priori sur la structure du document modélisées. Pour déterminer l'étiquetage optimal, un point clé de notre modèle est l'implémentation de l'algorithme d'inférence optimal de Programmation Dynamique 2D. Ce modèle a été testé sur 1250 lettres manuscrites de la base RIMES. De bons résultats ont été obtenus montrant la capacité de cette approche à extraire la mise en page d'un document complexe à partir d'informations de différentes natures. (morphologiques, spatiales, ...)
Url:
DOI: 10.4267/2042/29097
Affiliations:
Links toward previous steps (curation, corpus...)
- to stream Hal, to step Corpus: 000529
- to stream Hal, to step Curation: 000529
- to stream Hal, to step Checkpoint: 000496
- to stream Main, to step Merge: 000A91
- to stream Main, to step Curation: 000A83
Le document en format XML
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Dans cette approche, l'extraction de la mise en page est considérée comme une tâche d'étiquetage consistant à assigner une étiquette à chaque pixel de l'image du document. Le modèle CAC donne directement accès à la probabilité conditionnelle globale d'un étiquetage de l'image sachant des caractéristiques image et des connaissances a priori sur la structure du document modélisées. Pour déterminer l'étiquetage optimal, un point clé de notre modèle est l'implémentation de l'algorithme d'inférence optimal de Programmation Dynamique 2D. Ce modèle a été testé sur 1250 lettres manuscrites de la base RIMES. De bons résultats ont été obtenus montrant la capacité de cette approche à extraire la mise en page d'un document complexe à partir d'informations de différentes natures. (morphologiques, spatiales, ...)</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Haute-Normandie</li><li>Région Normandie</li></region><settlement><li>Le Havre</li><li>Rouen</li></settlement><orgName><li>Université de Rouen</li><li>Université du Havre</li></orgName></list><tree><country name="France"><region name="Région Normandie"><name sortKey="Montreuil, Florent" sort="Montreuil, Florent" uniqKey="Montreuil F" first="Florent" last="Montreuil">Florent Montreuil</name></region><name sortKey="Grosicki, Emmanuele" sort="Grosicki, Emmanuele" uniqKey="Grosicki E" first="Emmanuèle" last="Grosicki">Emmanuèle Grosicki</name><name sortKey="Heutte, Laurent" sort="Heutte, Laurent" uniqKey="Heutte L" first="Laurent" last="Heutte">Laurent Heutte</name><name sortKey="Nicolas, Stephane" sort="Nicolas, Stephane" uniqKey="Nicolas S" first="Stéphane" last="Nicolas">Stéphane Nicolas</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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