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Communication Dans Un Congrès Année : 2019

KeyWord Spotting using Siamese Triplet Deep Neural Networks

Yasmine Serdouk
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  • PersonId : 1048825

Résumé

Deep neural networks has shown great success incomputer vision fields by achieving considerable state-of-the-artresults and are beginning to arouse big interest in the documentanalysis community. In this paper, we present a novel siamesedeep network of three inputs that allows retrieving the mostsimilar words to a given query. The proposed system followsa query-by-example approach according to a segmentation-based technique and aims to learn suitable representations ofhandwritten word images, for which a simple Euclidean distancecould perform the matching. The results obtained for the GeorgeWashington dataset show the potential and the effectiveness ofthe proposed keyword spotting system
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Dates et versions

hal-02155381 , version 1 (13-06-2019)

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Véronique Eglin, Yasmine Serdouk, Stéphane Bres, Mylène Pardoen. KeyWord Spotting using Siamese Triplet Deep Neural Networks. International Conference on Document Analysis and Recognition, ICDAR, Sep 2019, Sydney, Australia. ⟨10.1109/ICDAR.2019.00187⟩. ⟨hal-02155381⟩
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