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Historical document binarization combining semantic labeling and graph cuts
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
2017 (English)In: Image Analysis: Part I, Springer, 2017, p. 386-396Conference paper, Published paper (Refereed)
Abstract [en]

Most data mining applications on collections of historical documents require binarization of the digitized images as a pre-processing step. Historical documents are often subjected to degradations such as parchment aging, smudges and bleed through from the other side. The text is sometimes printed, but more often handwritten. Mathematical modeling of appearance of the text, background and all kinds of degradations, is challenging. In the current work we try to tackle binarization as pixel classification problem. We first apply semantic segmentation, using fully convolutional neural networks. In order to improve the sharpness of the result, we then apply a graph cut algorithm. The labels from the semantic segmentation are used as approximate estimates of the text and background, with the probability map of background used for pruning the edges in the graph cut. The results obtained show significant improvement over the state of the art approach.

Place, publisher, year, edition, pages
Springer, 2017. p. 386-396
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10269
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-335335DOI: 10.1007/978-3-319-59126-1_32ISI: 000454359300032ISBN: 978-3-319-59125-4 (print)OAI: oai:DiVA.org:uu-335335DiVA, id: diva2:1162457
Conference
SCIA 2017, June 12–14, Tromsø, Norway
Funder
Swedish Research Council, 2012-5743Riksbankens Jubileumsfond, NHS14-2068:1Available from: 2017-05-19 Created: 2017-12-04 Last updated: 2019-03-19Bibliographically approved
In thesis
1. Learning based segmentation and generation methods for handwritten document images
Open this publication in new window or tab >>Learning based segmentation and generation methods for handwritten document images
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
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.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2019. p. 97
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1783
Keywords
Machine learning, handwriting, handwritten document anlysis, deep learning, image processing
National Category
Computer Systems
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-379636 (URN)978-91-513-0599-8 (ISBN)
Public defence
2019-05-08, TLS, Carolina Rediviva Library, Dag Hammarskjölds Väg 1, Uppsala, 09:00 (English)
Opponent
Supervisors
Available from: 2019-04-15 Created: 2019-03-19 Last updated: 2019-06-17Bibliographically approved

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Ayyalasomayajula, Kalyan RamBrun, Anders

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