Text Recognition in Multimedia Documents: A Study of two Neural-based OCRs Using and Avoiding Character Segmentation
Identifieur interne : 000036 ( France/Analysis ); précédent : 000035; suivant : 000037Text Recognition in Multimedia Documents: A Study of two Neural-based OCRs Using and Avoiding Character Segmentation
Auteurs : Khaoula Elagouni [France] ; Christophe Garcia [France] ; Franck Mamalet [France] ; Pascale Sébillot [France]Source :
English descriptors
Abstract
Text embedded in multimedia documents represents an important semantic information that helps to automatically access the content. This paper proposes two neural-based OCRs that handle the text recognition problem in different ways. The first approach segments a text image into individual characters before recognizing them, while the second one avoids the segmentation step by integrating a multi-scale scanning scheme that allows to jointly localize and recognize characters at each position and scale. Some linguistic knowledge is also incorporated into the proposed schemes to remove errors due to recognition confusions. Both OCR systems are applied to caption texts embedded in videos and in natural scene images and provide outstanding results showing that the proposed approaches outperform the state-of-the-art methods.
Url:
DOI: 10.1007/s10032-013-0202-7
Affiliations:
- France
- Auvergne-Rhône-Alpes, Rhône-Alpes, Région Bretagne
- Lyon, Rennes
- Université Claude Bernard Lyon 1, Université de Lyon, Université de Rennes 1, Université européenne de Bretagne
Links toward previous steps (curation, corpus...)
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- to stream Main, to step Merge: 000077
- to stream Main, to step Curation: 000076
- to stream Main, to step Exploration: 000076
- to stream France, to step Extraction: 000036
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Hal:hal-00867225Le document en format XML
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This paper proposes two neural-based OCRs that handle the text recognition problem in different ways. The first approach segments a text image into individual characters before recognizing them, while the second one avoids the segmentation step by integrating a multi-scale scanning scheme that allows to jointly localize and recognize characters at each position and scale. Some linguistic knowledge is also incorporated into the proposed schemes to remove errors due to recognition confusions. Both OCR systems are applied to caption texts embedded in videos and in natural scene images and provide outstanding results showing that the proposed approaches outperform the state-of-the-art methods.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Auvergne-Rhône-Alpes</li><li>Rhône-Alpes</li><li>Région Bretagne</li></region><settlement><li>Lyon</li><li>Rennes</li></settlement><orgName><li>Université Claude Bernard Lyon 1</li><li>Université de Lyon</li><li>Université de Rennes 1</li><li>Université européenne de Bretagne</li></orgName></list><tree><country name="France"><noRegion><name sortKey="Elagouni, Khaoula" sort="Elagouni, Khaoula" uniqKey="Elagouni K" first="Khaoula" last="Elagouni">Khaoula Elagouni</name></noRegion><name sortKey="Garcia, Christophe" sort="Garcia, Christophe" uniqKey="Garcia C" first="Christophe" last="Garcia">Christophe Garcia</name><name sortKey="Mamalet, Franck" sort="Mamalet, Franck" uniqKey="Mamalet F" first="Franck" last="Mamalet">Franck Mamalet</name><name sortKey="Sebillot, Pascale" sort="Sebillot, Pascale" uniqKey="Sebillot P" first="Pascale" last="Sébillot">Pascale Sébillot</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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