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    This summary is machine-generated.

    This study introduces a new method for automatically estimating the date and location of historical documents by analyzing handwriting. This advancement aids historians in document provenance research.

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    Area of Science:

    • Digital Humanities
    • Computer Vision
    • Historical Document Analysis

    Background:

    • Accurate dating and localization of historical documents are crucial for historians.
    • Current methods for determining document provenance are often manual and time-consuming.

    Purpose of the Study:

    • To develop an automated method for estimating the geographical and temporal provenance of handwritten historical documents.
    • To explore the correlation between visual elements of handwriting and document labels (date, location).

    Main Methods:

    • A novel descriptor, histogram of orientations of handwritten strokes (HOS), is proposed for visual element extraction.
    • A multi-label self-organizing map (MLSOM) is utilized to correlate visual features with abstract labels.
    • The MLSOM facilitates direct label prediction and codebook construction for enhanced discrimination.

    Main Results:

    • The proposed method demonstrates state-of-the-art performance on a medieval paleographic dataset.
    • The MLSOM effectively discovers correlations between low-level visual features and document provenance labels.
    • The HOS descriptor captures relevant visual information for provenance estimation.

    Conclusions:

    • The developed automated approach significantly advances historical document analysis.
    • Handwriting style analysis using HOS and MLSOM is a viable method for determining document date and place of origin.
    • This research offers a powerful tool for historical scholars in document provenance research.