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    <title>Neural text line extraction in historical documents</title>
    <subTitle>a two-stage clustering approach</subTitle>
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    <namePart>Grüning, Tobias</namePart>
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  <abstract type="Summary">Accessibility of the valuable cultural heritage which is hidden in countless scanned historical documents is the motivation for the presented dissertation. The developed (fully automatic) text line extraction methodology combines state-of-the-art machine learning techniques and modern image processing methods. It demonstrates its quality by outperforming several other approaches on a couple of benchmarking datasets. The method is already being used by a wide audience of researchers from different disciplines and thus contributes its (small) part to the aforementioned goal.&lt;eng&gt;</abstract>
  <abstract type="Summary">Das Erschließen des unermesslichen Wissens, welches in unzähligen gescannten historischen Dokumenten verborgen liegt, bildet die Motivation für die vorgelegte Dissertation. Durch das Verknüpfen moderner Verfahren des maschinellen Lernens und der klassischen Bildverarbeitung wird in dieser Arbeit ein vollautomatisches Verfahren zur Extraktion von Textzeilen aus historischen Dokumenten entwickelt. Die Qualität wird auf verschiedensten Datensätzen im Vergleich zu anderen Ansätzen nachgewiesen. Das Verfahren wird bereits durch eine Vielzahl von Forschern verschiedenster Disziplinen genutzt.&lt;ger&gt;</abstract>
  <note type="statement of responsibility">vorgelegt von Tobias Grüning</note>
  <note>GutachterInnen: Roger Labahn (Universität Rostock, Institut für Mathematik) ; Basilis Gatos (Institute of Informatics and Telecommunications, National Center for Scientific Research “Demokritos”)</note>
  <note type="thesis">Dissertation Universität Rostock 2019</note>
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      <title>Neural text line extraction in historical documents</title>
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      <publisher>Rostock : Universität Rostock, 2018</publisher>
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      <namePart>Grüning, Tobias, 1986 - </namePart>
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