Automatic annotation extension and classification of documents using a probabilistic graphical model
Identifieur interne : 000387 ( Main/Exploration ); précédent : 000386; suivant : 000388Automatic annotation extension and classification of documents using a probabilistic graphical model
Auteurs : Abdessalem Bouzaieni [France] ; Sabine Barrat [France] ; Salvatore Tabbone [France]Source :
Abstract
With the fast growth of document images, the domain of document annotations has become aresearch area of great interest. Annotations allow to describe the semantic content of documents, and facilitatethe use and research task for the user. However, for a huge number of documents it is a tedious taskto annote each document manually. A solution is to annote a small part of the documents and to extendthis annotation automatically to the whole dataset. In this paper, we propose a model for annotation extensionand for documents classification using a probabilistic graphical model. In this model, we combine visual andtextual characteristics and we show that the integration of the user feedback improves significantly the results.
Url:
DOI: 10.1109/ICDAR.2015.7333775
Affiliations:
- France
- Centre-Val de Loire, Grand Est, Lorraine (région), Région Centre
- Metz, Nancy, Tours
- Centre Val de Loire Université, Université de Lorraine, Université de Tours
Links toward previous steps (curation, corpus...)
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- to stream Hal, to step Checkpoint: 000364
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- to stream Main, to step Curation: 000387
Le document en format XML
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Annotations allow to describe the semantic content of documents, and facilitatethe use and research task for the user. However, for a huge number of documents it is a tedious taskto annote each document manually. A solution is to annote a small part of the documents and to extendthis annotation automatically to the whole dataset. In this paper, we propose a model for annotation extensionand for documents classification using a probabilistic graphical model. In this model, we combine visual andtextual characteristics and we show that the integration of the user feedback improves significantly the results.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Centre-Val de Loire</li><li>Grand Est</li><li>Lorraine (région)</li><li>Région Centre</li></region><settlement><li>Metz</li><li>Nancy</li><li>Tours</li></settlement><orgName><li>Centre Val de Loire Université</li><li>Université de Lorraine</li><li>Université de Tours</li></orgName></list><tree><country name="France"><region name="Grand Est"><name sortKey="Bouzaieni, Abdessalem" sort="Bouzaieni, Abdessalem" uniqKey="Bouzaieni A" first="Abdessalem" last="Bouzaieni">Abdessalem Bouzaieni</name></region><name sortKey="Barrat, Sabine" sort="Barrat, Sabine" uniqKey="Barrat S" first="Sabine" last="Barrat">Sabine Barrat</name><name sortKey="Tabbone, Salvatore" sort="Tabbone, Salvatore" uniqKey="Tabbone S" first="Salvatore" last="Tabbone">Salvatore Tabbone</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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