Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Spotting words in medieval manuscripts
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 Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
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: Studia Neophilologica, ISSN 0039-3274, E-ISSN 1651-2308, Vol. 86, 171-186 p.Article in journal (Refereed) Published
Abstract [en]

This article discusses the technology of handwritten text recognition (HTR) as a tool for the analysis of historical handwritten documents. We give a broad overview of this field of research, but the focus is on the use of a method called word spotting' for finding words directly and automatically in scanned images of manuscript pages. We illustrate and evaluate this method by applying it to a medieval manuscript. Word spotting uses digital image analysis to represent stretches of writing as sequences of numerical features. These are intended to capture the linguistically significant aspects of the visual shape of the writing. Two potential words can then be compared mathematically and their degree of similarity assigned a value. Our version of this method gives a false positive rate of about 30%, when the true positive rate is close to 100%, for an application where we search for very frequent short words in a 16th-Century Old Swedish cursiva recentior manuscript. Word spotting would be of use e.g. to researchers who want to explore the content of manuscripts when editions or other transcriptions are unavailable.

Place, publisher, year, edition, pages
2014. Vol. 86, 171-186 p.
National Category
Computer and Information Science General Language Studies and Linguistics Language Technology (Computational Linguistics)
Research subject
Computational Linguistics
Identifiers
URN: urn:nbn:se:uu:diva-227725DOI: 10.1080/00393274.2013.871975ISI: 000335850200012OAI: oai:DiVA.org:uu-227725DiVA: diva2:730977
Available from: 2014-01-20 Created: 2014-06-30 Last updated: 2017-12-05Bibliographically 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

Open Access in DiVA

fulltext(371 kB)404 downloads
File information
File name FULLTEXT01.pdfFile size 371 kBChecksum SHA-512
3f912a1ec8da3e11664057fc7381d0be900f5edc7112d14c10a22b40d3199efba6754fdb7871b51c05dd05b0f4cb169c3202c2dc0f80c3a56d77e3c15e35fc82
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Search in DiVA

By author/editor
Wahlberg, FredrikDahllöf, MatsBrun, Anders
By organisation
Division of Visual Information and InteractionComputerized Image Analysis and Human-Computer InteractionDepartment of Linguistics and Philology
In the same journal
Studia Neophilologica
Computer and Information ScienceGeneral Language Studies and LinguisticsLanguage Technology (Computational Linguistics)

Search outside of DiVA

GoogleGoogle Scholar
Total: 404 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 522 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf