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Scribal Attribution using a Novel 3-D Quill-Curvature Feature Histogram
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.ORCID iD: 0000-0002-4405-6888
2014 (English)In: Proceedings International Conference on Frontiers in Handwriting Recognition (ICFHR), 2014, 2014Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we propose a novel pipeline forautomated scribal attribution based on the Quill feature: 1) Wecompensate the Quill feature histogram for pen changes andpage warping. 2) We add curvature as a third dimension in thefeature histogram, to better separate characteristics like loopsand lines. 3) We also investigate the use of several dissimilaritymeasures between the feature histograms. 4) We propose andevaluate semi-supervised learning for classification, to reducethe need of labeled samples.Our evaluation is performed on 1104 pages from a 15thcentury Swedish manuscript. It was chosen because it repre-sents a significant part of Swedish manuscripts of said period.Our results show that only a few percent of the materialneed labelling for average precisions above 95%. Our novelcurvature and registration extensions, together with semi-supervised learning, outperformed the current Quill feature.

Place, publisher, year, edition, pages
2014.
Keyword [en]
writer identification; semi-supervised learning; classification; historical manuscripts
National Category
Computer Science
Research subject
Computer Science; Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-238270OAI: oai:DiVA.org:uu-238270DiVA: diva2:770693
Conference
The International Conference on Frontiers in Handwriting Recognition (ICFHR), September 1-4, 2014, Crete, Greece
Projects
q2bq2b_vr2012
Funder
Swedish Research Council, 2012-5743
Available from: 2014-12-11 Created: 2014-12-11 Last updated: 2017-02-05Bibliographically approved
In thesis
1. Interpreting the Script: Image Analysis and Machine Learning for Quantitative Studies of Pre-modern Manuscripts
Open this publication in new window or tab >>Interpreting the Script: Image Analysis and Machine Learning for Quantitative Studies of Pre-modern Manuscripts
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The humanities have for a long time been a collection of fields that have not gained from the advancements in computational power, as predicted by Moore´s law.  Fields like medicine, biology, physics, chemistry, geology and economics have all developed quantitative tools that take advantage of the exponential increase of processing power over time.  Recent advances in computerized pattern recognition, in combination with a rapid digitization of historical document collections around the world, is about to change this.

The first part of this dissertation focuses on constructing a full system for finding handwritten words in historical manuscripts. A novel segmentation algorithm is presented, capable of finding and separating text lines in pre-modern manuscripts.  Text recognition is performed by translating the image data of the text lines into sequences of numbers, called features. Commonly used features are analysed and evaluated on manuscript sources from the Uppsala University library Carolina Rediviva and the US Library of Congress.  Decoding the text in the vast number of photographed manuscripts from our libraries makes computational linguistics and social network analysis directly applicable to historical sources. Hence, text recognition is considered a key technology for the future of computerized research methods in the humanities.

The second part of this thesis addresses digital palaeography, using a computers superior capacity for endlessly performing measurements on ink stroke shapes. Objective criteria of character shapes only partly catches what a palaeographer use for assessing similarity. The palaeographer often gets a feel for the scribe's style.  This is, however, hard to quantify.  A method for identifying the scribal hands of a pre-modern copy of the revelations of saint Bridget of Sweden, using semi-supervised learning, is presented.  Methods for production year estimation are presented and evaluated on a collection with close to 11000 medieval charters.  The production dates are estimated using a Gaussian process, where the uncertainty is inferred together with the most likely production year.

In summary, this dissertation presents several novel methods related to image analysis and machine learning. In combination with recent advances of the field, they enable efficient computational analysis of very large collections of historical documents.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2017. 95 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1475
Keyword
document analysis, machine learning, image analysis, digital humanities, document dating, writer identification, text recognition
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:uu:diva-314211 (URN)978-91-554-9814-6 (ISBN)
Public defence
2017-03-24, Tidskriftläsesalen, Carolina rediviva, Dag Hammarskjölds väg 1, Uppsala, 10:15 (English)
Opponent
Supervisors
Projects
q2b
Available from: 2017-03-02 Created: 2017-01-31 Last updated: 2017-03-06

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Wahlberg, FredrikBrun, Anders

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