Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Graph Based Line Segmentation on Cluttered Handwritten Manuscripts
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.ORCID-id: 0000-0002-4405-6888
2012 (Engelska)Ingår i: Proceedings of the 21st International Conference on Pattern Recognition, 2012, IEEE , 2012, s. 1570-1573Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

We propose a two phase line segmentationmethod for handwritten pre-modern densely writ-ten manuscripts. The proposed method combinesthe robustness of projection based methods withthe flexibility of graph based methods. The resultare cut-outs of the image containing each text line.Overlapping characters, help lines and degradationcan create foreground elements spanning several linesthat are hard to separate. We treat the problem offinding a cut through the text line separation as agraph optimization problem, which allows for flexibleseparation of entangled components.The proposed method has been tested on two me-dieval sources with satisfying results. A comparison tosimilar methods, using standard metrics, is presented.

Ort, förlag, år, upplaga, sidor
IEEE , 2012. s. 1570-1573
Nationell ämneskategori
Datorseende och robotik (autonoma system)
Identifikatorer
URN: urn:nbn:se:uu:diva-188588ISBN: 978-1-4673-2216-4 (tryckt)OAI: oai:DiVA.org:uu-188588DiVA, id: diva2:578264
Konferens
21st International Conference on Pattern Recognition (ICPR), 2012
Tillgänglig från: 2012-12-17 Skapad: 2012-12-17 Senast uppdaterad: 2018-01-11Bibliografiskt granskad
Ingår i avhandling
1. Interpreting the Script: Image Analysis and Machine Learning for Quantitative Studies of Pre-modern Manuscripts
Öppna denna publikation i ny flik eller fönster >>Interpreting the Script: Image Analysis and Machine Learning for Quantitative Studies of Pre-modern Manuscripts
2017 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Uppsala: Acta Universitatis Upsaliensis, 2017. s. 95
Serie
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1475
Nyckelord
document analysis, machine learning, image analysis, digital humanities, document dating, writer identification, text recognition
Nationell ämneskategori
Datorseende och robotik (autonoma system)
Forskningsämne
Datavetenskap
Identifikatorer
urn:nbn:se:uu:diva-314211 (URN)978-91-554-9814-6 (ISBN)
Disputation
2017-03-24, Tidskriftläsesalen, Carolina rediviva, Dag Hammarskjölds väg 1, Uppsala, 10:15 (Engelska)
Opponent
Handledare
Projekt
q2b
Tillgänglig från: 2017-03-02 Skapad: 2017-01-31 Senast uppdaterad: 2018-01-13

Open Access i DiVA

fulltext(1672 kB)106 nedladdningar
Filinformation
Filnamn FULLTEXT01.pdfFilstorlek 1672 kBChecksumma SHA-512
1d8351dbed12b6fd250022e66309982e2d0eeab459b04ec7e750b7e2eeefdbcc538c9de69b355017dda204e69333b7346ecbca3345d75ba029d0c56c8bf06cbd
Typ fulltextMimetyp application/pdf

Sök vidare i DiVA

Av författaren/redaktören
Wahlberg, FredrikBrun, Anders
Av organisationen
Avdelningen för visuell information och interaktionBildanalys och människa-datorinteraktion
Datorseende och robotik (autonoma system)

Sök vidare utanför DiVA

GoogleGoogle Scholar
Totalt: 106 nedladdningar
Antalet nedladdningar är summan av nedladdningar för alla fulltexter. Det kan inkludera t.ex tidigare versioner som nu inte längre är tillgängliga.

isbn
urn-nbn

Altmetricpoäng

isbn
urn-nbn
Totalt: 506 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf