Authors:
Lisa Koopmans
;
Maruf A. Dhali
and
Lambert Schomaker
Affiliation:
Department of Artificial Intelligence, University of Groningen, The Netherlands
Keyword(s):
Data Augmentation, Document Analysis, Historical Manuscript Dating, Self-Organizing Maps, Neural Networks, Support Vector Machines.
Abstract:
Identifying the production dates of historical manuscripts is one of the main goals for paleographers when studying ancient documents. Automatized methods can provide paleographers with objective tools to estimate dates more accurately. Previously, statistical features have been used to date digitized historical manuscripts based on the hypothesis that handwriting styles change over periods. However, the sparse availability of such documents poses a challenge in obtaining robust systems. Hence, the research of this article explores the influence of data augmentation on the dating of historical manuscripts. Linear Support Vector Machines were trained with k-fold cross-validation on textural and grapheme-based features extracted from historical manuscripts of different collections, including the Medieval Paleographical Scale, early Aramaic manuscripts, and the Dead Sea Scrolls. Results show that training models with augmented data improve the performance of historical manuscripts datin
g by 1% - 3% in cumulative scores. Additionally, this indicates further enhancement possibilities by considering models specific to the features and the documents’ scripts.
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