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• W1888914491 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." @default.
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• W1888914491 date "2012-01-01" @default.
• W1888914491 modified "2023-10-16" @default.
• W1888914491 title "Text Recognition in Videos Using a Recurrent Connectionist Approach" @default.
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• W1888914491 doi "https://doi.org/10.1007/978-3-642-33266-1_22" @default.
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