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KeyWord Spotting using Siamese Triplet Deep Neural Networks

Abstract : 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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Contributor : Véronique Eglin <>
Submitted on : Thursday, June 13, 2019 - 3:17:09 PM
Last modification on : Wednesday, July 8, 2020 - 12:43:48 PM


  • HAL Id : hal-02155381, version 1


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. ⟨hal-02155381⟩



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