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Text Recognition in Videos using a Recurrent Connectionist Approach

Khaoula Elagouni 1 Christophe Garcia 2 Franck Mamalet 1 Pascale Sébillot 3
2 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
3 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
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.
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Submitted on : Monday, November 19, 2012 - 6:53:30 PM
Last modification on : Wednesday, June 16, 2021 - 3:35:01 AM
Long-term archiving on: : Thursday, February 21, 2013 - 11:46:33 AM


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Khaoula Elagouni, Christophe Garcia, Franck Mamalet, Pascale Sébillot. Text Recognition in Videos using a Recurrent Connectionist Approach. 22th International Conference on Artificial Neural Networks, ICANN, Sep 2012, Lausanne, Switzerland. pp.172-179, ⟨10.1007/978-3-642-33266-1_22⟩. ⟨hal-00753906⟩



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