  Digitized archives contain and preserve the knowledge of generations of
scholars in millions of documents. The size of these archives calls for
automatic analysis since a manual analysis by specialists is often too
expensive. In this paper, we focus on the analysis of handwriting in scanned
documents from the art-historic archive of the WPI. Since the archive consists
of documents written in several languages and lacks annotated training data for
the creation of recognition models, we propose the task of handwriting
classification as a new step for a handwriting OCR pipeline. We propose a
handwriting classification model that labels extracted text fragments, eg,
numbers, dates, or words, based on their visual structure. Such a
classification supports historians by highlighting documents that contain a
specific class of text without the need to read the entire content. To this
end, we develop and compare several deep learning-based models for text
classification. In extensive experiments, we show the advantages and
disadvantages of our proposed approach and discuss possible usage scenarios on
a real-world dataset.
