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Embedded Prototype Subspace Classification: A Subspace Learning Framework

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Computer Analysis of Images and Patterns (CAIP 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11679))

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Abstract

Handwritten text recognition is a daunting task, due to complex characteristics of handwritten letters. Deep learning based methods have achieved significant advances in recognizing challenging handwritten texts because of its ability to learn and accurately classify intricate patterns. However, there are some limitations of deep learning, such as lack of well-defined mathematical model, black-box learning mechanism, etc., which pose challenges. This paper aims at going beyond the black-box learning and proposes a novel learning framework called as Embedded Prototype Subspace Classification, that is based on the well-known subspace method, to recognise handwritten letters in a fast and efficient manner. The effectiveness of the proposed framework is empirically evaluated on popular datasets using standard evaluation measures.

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Authors and Affiliations

  1. Division of Visual Information and Interaction, Department of Information Technology, Uppsala University, 751 05, Uppsala, Sweden

    Anders Hast, Mats Lind & Ekta Vats

Authors
  1. Anders Hast
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  2. Mats Lind
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  3. Ekta Vats
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Corresponding author

Correspondence to Anders Hast .

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Editors and Affiliations

  1. Department of Computer and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano (SA), Italy

    Mario Vento

  2. Department of Computer and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano (SA), Italy

    Gennaro Percannella

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Hast, A., Lind, M., Vats, E. (2019). Embedded Prototype Subspace Classification: A Subspace Learning Framework. In: Vento, M., Percannella, G. (eds) Computer Analysis of Images and Patterns. CAIP 2019. Lecture Notes in Computer Science(), vol 11679. Springer, Cham. https://doi.org/10.1007/978-3-030-29891-3_51

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  • DOI: https://doi.org/10.1007/978-3-030-29891-3_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29890-6

  • Online ISBN: 978-3-030-29891-3

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