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dc.contributor.authorChristlein, Vincent
dc.contributor.authorWeichselbaumer, Nikolaus
dc.contributor.authorLimbach, Saskia
dc.contributor.authorSeuret, Mathias
dc.contributor.editorReussner, Ralf H.
dc.contributor.editorKoziolek, Anne
dc.contributor.editorHeinrich, Robert
dc.date.accessioned2021-01-27T13:33:21Z
dc.date.available2021-01-27T13:33:21Z
dc.date.issued2021
dc.identifier.isbn978-3-88579-701-2
dc.identifier.issn1617-5468
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/34712
dc.description.abstractThe type used to print an early modern book can give scholars valuable information about the time and place of its production as well as its producer. Recognizing such type is currently done manually using both the character shapes of 'M' or 'Qu' and the size of the total type to look it up in a large reference work. This is a reliable method, but it is also slow and requires specific skills. We investigate the performance of type classification and type retrieval using a newly created dataset consisting of easy and difficult types used in early printed books. For type classification, we rely on a deep Convolutional Neural Network (CNN) originally used for font-group classification while we use a common writer identification method for the retrieval case. We show that in both scenarios, easy types can be classified/retrieved with a high accuracy while difficult cases are indeed difficult.en
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofINFORMATIK 2020
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-307
dc.subjecttype recognition
dc.subjecttype classification
dc.subjecttype retrieval
dc.subjectdeep learning
dc.titleProof of Concept: Automatic Type Recognitionen
mci.reference.pages1307-1316
mci.conference.sessiontitleMethoden und Anwendungen der Computational Humanities
mci.conference.locationKarlsruhe
mci.conference.date28. September - 2. Oktober 2020
dc.identifier.doi10.18420/inf2020_122


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