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Historical Manuscript Production Date Estimation using Deep Convolutional Neural Networks
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
2016 (English)Conference paper (Refereed)
Abstract [en]

Deep learning has thus far not been used for dating of pre-modern handwritten documents. In this paper, we propose ways of using deep convolutional neural networks (CNNs) to estimate production dates for such manuscripts. In our approach, a CNN can either be used directly for estimating the production date or as a feature learning framework for other regression techniques. We explore the feature learning approach using Gaussian Processes regression and Support Vector Regression.The evaluation is performed on a unique large dataset of over 10000 medieval charters from the Swedish collection Svenskt Diplomatariums huvudkartotek (SDHK). We show that deep learning is applicable to the task of dating documents and that the performance is on average comparable to that of a human expert.

Place, publisher, year, edition, pages
2016.
Keyword [en]
Document analysis, Manuscripts, Document dating, Digital Humanities
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-306685OAI: oai:DiVA.org:uu-306685DiVA: diva2:1044057
Conference
International Conference on Frontiers in Handwriting Recognition (ICFHR), October 23-26, 2016, Shenzhen, China.
Projects
q2b
Funder
Swedish Research Council, 2012-5743Riksbankens Jubileumsfond, NHS14-2068:1
Available from: 2016-11-01 Created: 2016-11-01 Last updated: 2016-11-03

Open Access in DiVA

fulltext(1349 kB)18 downloads
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Type fulltextMimetype application/pdf

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Wahlberg, FredrikWilkinson, TomasBrun, Anders
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ReferencesLink to record
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