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  • 1.
    Kaatari, Henrik
    Uppsala universitet, Engelska institutionen.
    Review of Ilka Mindt: Adjective complementation: An empirical analysis of adjectives followed by that–clauses (Studies in Corpus Linguistics 42). Amsterdam and Philadelphia: John Benjamins, 20112012In: Studia Neophilologica, ISSN 0039-3274, E-ISSN 1651-2308, Vol. 84, no 1, p. 120-124Article, book review (Other academic)
  • 2.
    Kaatari, Henrik
    Uppsala universitet, Engelska institutionen.
    Review of Juhani Rudanko: Changes in Complementation in British and American English: Corpus-based Studies on Non-finite Complements in Recent English2013In: Studia Neophilologica, ISSN 0039-3274, E-ISSN 1651-2308, Vol. 85, no 2, p. 241-244Article, book review (Other academic)
  • 3.
    Wahlberg, Fredrik
    et al.
    Centrum för bildanalys, Uppsala universitet/Sveriges lantbruksuniversitet.
    Dahllöf, Mats
    Inst. för lingvistik och filologi, Uppsala universitet.
    Mårtensson, Lasse
    University of Gävle, Faculty of Education and Business Studies, Department of Humanities, Swedish.
    Brun, Anders
    Centrum för bildanalys, Uppsala universitet/Sveriges lantbruksuniversitet.
    Spotting Words in Medieval Manuscripts2014In: Studia Neophilologica, ISSN 0039-3274, E-ISSN 1651-2308, Vol. 86, no 1, p. 1-16Article in journal (Refereed)
    Abstract [en]

    This article discusses the technology of handwritten text recognition (HTR) as a tool for the analysis of historical handwritten documents. We give a broad overview of this field of research, but the focus is on the use of a method called ‘word spotting’ for finding words directly and automatically in scanned images of manuscript pages. We illustrate and evaluate this method by applying it to a medieval manuscript. Word spotting uses digital image analysis to represent stretches of writing as sequences of numerical features. These are intended to capture the linguistically significant aspects of the visual shape of the writing. Two potential words can then be compared mathematically and their degree of similarity assigned a value. Our version of this method gives a false positive rate of about 30%, when the true positive rate is close to 100%, for an application where we search for very frequent short words in a 16th-Century Old Swedish cursiva recentior manuscript. Word spotting would be of use e.g. to researchers who want to explore the content of manuscripts when editions or other transcriptions are unavailable.

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