Kleber, F., Peer, M., & Sablatnig, R. (2023, November 3). Advances in Machine Learning for the Automated Analysis of Manuscripts [Conference Presentation]. Written Heritage: New Challenges and Perspectives, Austria.
Due to the increasing digitization initiatives of archives/museums/…, the number of
digitally available documents, including historical manuscripts, is growing, facilitating access
for experts and humanities scholars. Advances in machine learning, particularly in the area
of deep learning, enable automated recognition of handwritten text, layout analysis, writer
retrieval, and research-specific tasks. Applications such as handwritten text recognition
allow on the one hand also ordinary persons to read historical scripts, and on the other
hand a text search in a large text corpus for experts without the necessity to read
everything. Additionally, writer retrieval enables to cluster documents based on the scribe.
Manuscripts and their metadata can also be modeled in knowledge bases and knowledge
representation learning can be used to produce interpretations automatically. Several state-
of-the-art ML methods in the context of research projects will be presented to give an
overview of the feasibility of automated methods.
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Research Areas:
Visual Computing and Human-Centered Technology: 100%
