OcrV1, Main, Exploration, bibRecord, 000224

Text Recognition in Videos using a Recurrent Connectionist Approach

Identifieur interne : 000224 ( Main/Exploration ); précédent : 000223; suivant : 000225

Text Recognition in Videos using a Recurrent Connectionist Approach

Auteurs : Khaoula Elagouni [France] ; Christophe Garcia [France] ; Franck Mamalet [France] ; Pascale Sébillot [France]

Source :

RBID : Hal:hal-00753906

Abstract

Most OCR (Optical Character Recognition) systems developed to recognize texts embedded in multimedia documents segment the text into characters before recognizing them. In this paper, we propose a novel approach able to avoid any explicit character segmentation. Using a multi-scale scanning scheme, texts extracted from videos are first represented by sequences of learnt features. Obtained representations are then used to feed a connectionist recurrent model specifically designed to take into account dependencies between successive learnt features and to recognize texts. The proposed video OCR evaluated on a database of TV news videos achieves very high recognition rates. Experiments also demonstrate that, for our recognition task, learnt feature representations perform better than hand-crafted features.

Url:

https://hal.archives-ouvertes.fr/hal-00753906

DOI: 10.1007/978-3-642-33266-1_22

Affiliations:

Links toward previous steps (curation, corpus...)

to stream Hal, to step Corpus: 000117
to stream Hal, to step Curation: 000117
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to stream Main, to step Merge: 000228
to stream Main, to step Curation: 000224

Le document en format XML

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<front><div type="abstract" xml:lang="en">Most OCR (Optical Character Recognition) systems developed to recognize texts embedded in multimedia documents segment the text into characters before recognizing them. In this paper, we propose a novel approach able to avoid any explicit character segmentation. Using a multi-scale scanning scheme, texts extracted from videos are first represented by sequences of learnt features. Obtained representations are then used to feed a connectionist recurrent model specifically designed to take into account dependencies between successive learnt features and to recognize texts. The proposed video OCR evaluated on a database of TV news videos achieves very high recognition rates. Experiments also demonstrate that, for our recognition task, learnt feature representations perform better than hand-crafted features.</div>
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<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>

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   |wiki=    Ticri/CIDE
   |area=    OcrV1
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   |étape=   Exploration
   |type=    RBID
   |clé=     Hal:hal-00753906
   |texte=   Text Recognition in Videos using a Recurrent Connectionist Approach
}}

This area was generated with Dilib version V0.6.32.
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Text Recognition in Videos using a Recurrent Connectionist Approach

Text Recognition in Videos using a Recurrent Connectionist Approach

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Abstract

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