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A Modified Isomap Approach to Manifold Learning in Word Spotting

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Pattern Recognition (DAGM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9358))

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Abstract

Word spotting is an effective paradigm for indexing document images with minimal human effort. Here, the use of the Bag-of-Features principle has been shown to achieve competitive results on different benchmarks. Recently, a spatial pyramid approach was used as a word image representation to improve the retrieval results even further. The high dimensionality of the spatial pyramids was attempted to be countered by applying Latent Semantic Analysis. However, this leads to increasingly worse results when reducing to lower dimensions. In this paper, we propose a new approach to reducing the dimensionality of word image descriptors which is based on a modified version of the Isomap Manifold Learning algorithm. This approach is able to not only outperform Latent Semantic Analysis but also to reduce a word image descriptor to up to \(0.12\,\%\) of its original size without losing retrieval precision. We evaluate our approach on two different datasets.

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Notes

  1. 1.

    http://transcriptorium.eu/~icdar15kws/data.html.

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Authors and Affiliations

  1. Technische Universität Dortmund, Dortmund, Germany

    Sebastian Sudholt & Gernot A. Fink

Authors
  1. Sebastian Sudholt
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  2. Gernot A. Fink
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Corresponding author

Correspondence to Sebastian Sudholt .

Editor information

Editors and Affiliations

  1. Institute of Computer Science III, University of Bonn, Bonn, Germany

    Juergen Gall

  2. MPI for Intelligent Systems, University of Tübingen, Tübingen, Germany

    Peter Gehler

  3. Computer Vision Group, Visual Computing Institute, RWTH Aachen, Aachen, Germany

    Bastian Leibe

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Sudholt, S., Fink, G.A. (2015). A Modified Isomap Approach to Manifold Learning in Word Spotting. In: Gall, J., Gehler, P., Leibe, B. (eds) Pattern Recognition. DAGM 2015. Lecture Notes in Computer Science(), vol 9358. Springer, Cham. https://doi.org/10.1007/978-3-319-24947-6_44

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  • DOI: https://doi.org/10.1007/978-3-319-24947-6_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24946-9

  • Online ISBN: 978-3-319-24947-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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