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Search: L773:9783030298913 OR L773:9783030298906

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  • Hast, Anders, et al. (author)
  • Embedded Prototype Subspace Classification : A subspace learning framework
  • 2019
  • In: Computer Analysis of Images and Patterns, CAIP 2019, PT II. - Cham : Springer. - 9783030298913 - 9783030298906 ; , s. 581-592
  • Conference paper (peer-reviewed)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 blackbox 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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Type of publication
conference paper (1)
Type of content
peer-reviewed (1)
Author/Editor
Hast, Anders (1)
Lind, Mats (1)
Vats, Ekta (1)
University
Uppsala University (1)
Language
English (1)
Research subject (UKÄ/SCB)
Natural sciences (1)
Engineering and Technology (1)
Year
2019 (1)

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