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Keyword Spotting with Convolutional Deep Belief Networks and Dynamic Time Warping

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Artificial Neural Networks and Machine Learning – ICANN 2016 (ICANN 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9887))

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Abstract

To spot keywords on handwritten documents, we present a hybrid keyword spotting system, based on features extracted with Convolutional Deep Belief Networks and using Dynamic Time Warping for word scoring. Features are learned from word images, in an unsupervised manner, using a sliding window to extract horizontal patches. For two single writer historical data sets, it is shown that the proposed learned feature extractor outperforms two standard sets of features.

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Notes

  1. 1.

    http://trec.nist.gov/trec_eval.

  2. 2.

    https://github.com/wichtounet/word_spotting/tree/paper_v2.

  3. 3.

    https://github.com/wichtounet/dll.

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Authors and Affiliations

  1. University of Applied Science of Western Switerzland, Delémont, Switzerland

    Baptiste Wicht, Andreas Fischer & Jean Hennebert

  2. University of Fribourg, Fribourg, Switzerland

    Baptiste Wicht, Andreas Fischer & Jean Hennebert

Authors
  1. Baptiste Wicht
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  2. Andreas Fischer
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  3. Jean Hennebert
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Corresponding author

Correspondence to Baptiste Wicht .

Editor information

Editors and Affiliations

  1. University of Lausanne, Lausanne, Switzerland

    Alessandro E.P. Villa

  2. University of Lausanne, Lausanne, Switzerland

    Paolo Masulli

  3. Universitat Politécnica de Catalunya, Terrrassa, Spain

    Antonio Javier Pons Rivero

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Wicht, B., Fischer, A., Hennebert, J. (2016). Keyword Spotting with Convolutional Deep Belief Networks and Dynamic Time Warping. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9887. Springer, Cham. https://doi.org/10.1007/978-3-319-44781-0_14

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  • DOI: https://doi.org/10.1007/978-3-319-44781-0_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44780-3

  • Online ISBN: 978-3-319-44781-0

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