uu.seUppsala universitets publikasjoner
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Feature evaluation for handwritten character recognition with regressive and generative Hidden Markov Models
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 beräkningsvetenskap.ORCID-id: 0000-0003-0458-6902
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
2016 (engelsk)Inngår i: Advances in Visual Computing: Part I, Springer, 2016, s. 278-287Konferansepaper, Publicerat paper (Fagfellevurdert)
sted, utgiver, år, opplag, sider
Springer, 2016. s. 278-287
Serie
Lecture Notes in Computer Science ; 10072
HSV kategori
Forskningsprogram
Datoriserad bildbehandling
Identifikatorer
URN: urn:nbn:se:uu:diva-308662DOI: 10.1007/978-3-319-50835-1_26ISBN: 978-3-319-50834-4 (tryckt)OAI: oai:DiVA.org:uu-308662DiVA, id: diva2:1050536
Konferanse
ISVC 2016, December 12–14, Las Vegas, NV
Prosjekter
q2b – From Quill to BytesTilgjengelig fra: 2016-12-10 Laget: 2016-11-29 Sist oppdatert: 2019-03-19bibliografisk kontrollert
Inngår i avhandling
1. Learning based segmentation and generation methods for handwritten document images
Åpne denne publikasjonen i ny fane eller vindu >>Learning based segmentation and generation methods for handwritten document images
2019 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Computerized analysis of handwritten documents is an active research area in image analysis and computer vision. The goal is to create tools that can be available for use at university libraries and for researchers in the humanities. Working with large collections of handwritten documents is very time consuming and many old books and letters remain unread for centuries. Efficient computerized methods could help researchers in history, philology and computer linguistics to cost-effectively conduct a whole new type of research based on large collections of documents. The thesis makes a contribution to this area through the development of methods based on machine learning. The passage of time degrades historical documents. Humidity, stains, heat, mold and natural aging of the materials for hundreds of years make the documents increasingly difficult to interpret. The first half of the dissertation is therefore focused on cleaning the visual information in these documents by image segmentation methods based on energy minimization and machine learning. However, machine learning algorithms learn by imitating what is expected of them. One prerequisite for these methods to work is that ground truth is available. This causes a problem for historical documents because there is a shortage of experts who can help to interpret and interpret them. The second part of the thesis is therefore about automatically creating synthetic documents that are similar to handwritten historical documents. Because they are generated from a known text, they have a given facet. The visual content of the generated historical documents includes variation in the writing style and also imitates degradation factors to make the images realistic. When machine learning is trained on synthetic images of handwritten text, with a known facet, in many cases they can even give an even better result for real historical documents.

sted, utgiver, år, opplag, sider
Uppsala: Acta Universitatis Upsaliensis, 2019. s. 97
Serie
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1783
Emneord
Machine learning, handwriting, handwritten document anlysis, deep learning, image processing
HSV kategori
Forskningsprogram
Datoriserad bildbehandling
Identifikatorer
urn:nbn:se:uu:diva-379636 (URN)978-91-513-0599-8 (ISBN)
Disputas
2019-05-08, TLS, Carolina Rediviva Library, Dag Hammarskjölds Väg 1, Uppsala, 09:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2019-04-15 Laget: 2019-03-19 Sist oppdatert: 2019-06-17bibliografisk kontrollert

Open Access i DiVA

fulltext(726 kB)150 nedlastinger
Filinformasjon
Fil FULLTEXT01.pdfFilstørrelse 726 kBChecksum SHA-512
5dddb77273d6270efda17246d5085ba9fcb12a8fd66fdb604344e669d4473d15db51e743ea1d0a3825257b6fb6fb04e64b5820fdcef0d2cb3638103e2ee44ed4
Type fulltextMimetype application/pdf

Andre lenker

Forlagets fulltekst

Personposter BETA

Ayyalasomayajula, Kalyan RamNettelblad, CarlBrun, Anders

Søk i DiVA

Av forfatter/redaktør
Ayyalasomayajula, Kalyan RamNettelblad, CarlBrun, Anders
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 150 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

doi
isbn
urn-nbn

Altmetric

doi
isbn
urn-nbn
Totalt: 659 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf