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Statistical Relational Learning for Handwriting Recognition

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Inductive Logic Programming

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9046))

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Abstract

We introduce a novel application of handwriting recognition for Statistical Relational Learning. The proposed framework captures the intrinsic structure of handwriting by modeling fundamental character shape representations and their relationships using first-order logic. Our framework consists of three stages, (1) character extraction (2) feature generation and (3) class label prediction. In the character extraction stage, handwriting trajectory data is decoded into characters. Following this, character features (predicates) are defined across multiple levels - global, local and aggregated. Finally, a relational One-vs-All classifier is learned using relational functional gradient boosting (RFGB). We evaluate our approach on two datasets and demonstrate comparable accuracy to a well-established, meticulously engineered approach in the handwriting recognition paradigm.

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Author information

Authors and Affiliations

  1. University at Buffalo - SUNY, Buffalo, USA

    Arti Shivram & Venu Govindaraju

  2. University of Wisconsin-Madison, Madison, USA

    Tushar Khot

  3. Indiana University, Bloomington, USA

    Sriraam Natarajan

Authors
  1. Arti Shivram
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  2. Tushar Khot
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  3. Sriraam Natarajan
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  4. Venu Govindaraju
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Corresponding author

Correspondence to Arti Shivram .

Editor information

Editors and Affiliations

  1. Department of Computer Science, KU Leuven, Leuven, Belgium

    Jesse Davis

  2. Department of Computer Science, KU Leuven, Leuven, Belgium

    Jan Ramon

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Shivram, A., Khot, T., Natarajan, S., Govindaraju, V. (2015). Statistical Relational Learning for Handwriting Recognition. In: Davis, J., Ramon, J. (eds) Inductive Logic Programming. Lecture Notes in Computer Science(), vol 9046. Springer, Cham. https://doi.org/10.1007/978-3-319-23708-4_9

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  • DOI: https://doi.org/10.1007/978-3-319-23708-4_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23707-7

  • Online ISBN: 978-3-319-23708-4

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