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Document binarization using topological clustering guided Laplacian Energy Segmentation
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
2014 (engelsk)Inngår i: Proceedings International Conference on Frontiers in Handwriting Recognition (ICFHR), 2014, 2014, s. 523-528Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The current approach for text binarization proposesa clustering algorithm as a preprocessing stage toan energy-based segmentation method. It uses a clusteringalgorithm to obtain a coarse estimate of the background (BG)and foreground (FG) pixels. These estimates are used as a priorfor the source and sink points of a graph cut implementation,which is used to efficiently find the minimum energy solution ofan objective function to separate the BG and FG. The binaryimage thus obtained is used to refine the edge map that guidesthe graph cut algorithm. A final binary image is obtained byonce again performing the graph cut guided by the refinededges on a Laplacian of the image.

sted, utgiver, år, opplag, sider
2014. s. 523-528
Serie
Frontiers in Handwriting Recognition, ISSN 2167-6445 ; 14
Emneord [en]
Image Processing; Classification; Machine Learning; Graph-theoretic methods.
HSV kategori
Forskningsprogram
Datavetenskap
Identifikatorer
URN: urn:nbn:se:uu:diva-238316DOI: 10.1109/ICFHR.2014.94ISBN: 978-1-4799-4335-7 (tryckt)OAI: oai:DiVA.org:uu-238316DiVA, id: diva2:770839
Konferanse
International Conference on Frontiers in Handwriting Recognition (ICFHR),September 1-4, 2014, Crete, Greece.
Forskningsfinansiär
Swedish Research Council, 2012-5743Tilgjengelig fra: 2014-12-11 Laget: 2014-12-11 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

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