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  • 1.
    Hast, Anders
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Mårtensson, Lasse
    Vats, Ekta
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Heil, Raphaela
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Creating an Atlas over Handwritten Script Signs2019In: Digital Humanities in the Nordic Countries, 2019Conference paper (Refereed)
  • 2.
    Heil, Raphaela
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Vats, Ekta
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Hast, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Exploring the Applicability of Capsule Networks for WordSpotting in Historical Handwritten Manuscripts2018Conference paper (Other academic)
  • 3.
    Heil, Raphaela
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Vats, Ekta
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Hast, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Word Spotting in Historical Handwritten Manuscripts using Capsule Networks2018Conference paper (Other academic)
    Abstract [en]

    Word spotting is popularly used for digitisation and transcription of historical handwritten documents. Recently, deep learning based methods have dominated the current state-of-the-art in learning-based word spotting. However, deep learning architectures such as Convolutional Neural Networks (CNNs) require a large amount of training data, and suffer from translation invariance. Capsule Networks (CapsNet) have been recently introduced as a data-efficient alternative to CNNs. This work explores the applicability of CapsNets for segmentation-based word spotting, and is the first such effort in the Handwritten Text Recognition (HTR) community to the best of authors' knowledge. The effectiveness of CapsNets will be empirically evaluated on well-known historical handwritten datasets using standard evaluation measures. The impact of varying amounts of training data on the recognition performance will be investigated, along with a comparison with the state-of-the-art methods.

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