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Handwritten document image segmentation into text lines and words

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dc.contributor.author Papavassiliou, V en
dc.contributor.author Stafylakis, T en
dc.contributor.author Katsouros, V en
dc.contributor.author Carayannis, G en
dc.date.accessioned 2014-03-01T01:33:35Z
dc.date.available 2014-03-01T01:33:35Z
dc.date.issued 2010 en
dc.identifier.issn 0031-3203 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/20471
dc.subject Document image processing en
dc.subject Handwritten text line segmentation en
dc.subject Handwritten word segmentation en
dc.subject Support vector machines en
dc.subject Viterbi estimation en
dc.subject.classification Computer Science, Artificial Intelligence en
dc.subject.classification Engineering, Electrical & Electronic en
dc.subject.other Connected component en
dc.subject.other Data sets en
dc.subject.other Document image processing en
dc.subject.other Gap metrics en
dc.subject.other Handwriting segmentation en
dc.subject.other Handwritten document en
dc.subject.other Handwritten text line segmentation en
dc.subject.other Handwritten word segmentation en
dc.subject.other Line segmentation en
dc.subject.other Linear SVM en
dc.subject.other Objective functions en
dc.subject.other Text lines en
dc.subject.other Viterbi estimation en
dc.subject.other Word segmentation en
dc.subject.other Digital image storage en
dc.subject.other Image retrieval en
dc.subject.other Image segmentation en
dc.subject.other Imaging systems en
dc.subject.other Support vector machines en
dc.subject.other Viterbi algorithm en
dc.title Handwritten document image segmentation into text lines and words en
heal.type journalArticle en
heal.identifier.primary 10.1016/j.patcog.2009.05.007 en
heal.identifier.secondary http://dx.doi.org/10.1016/j.patcog.2009.05.007 en
heal.language English en
heal.publicationDate 2010 en
heal.abstract Two novel approaches to extract text lines and words from handwritten document are presented. The line segmentation algorithm is based on locating the optimal succession of text and gap areas within vertical zones by applying Viterbi algorithm. Then, a text-line separator drawing technique is applied and finally the connected components are assigned to text lines. Word segmentation is based on a gap metric that exploits the objective function of a soft-margin linear SVM that separates successive connected components. The algorithms tested on the benchmarking datasets of ICDAR07 handwriting segmentation contest and outperformed the participating algorithms. (C) 2009 Elsevier Ltd. All rights reserved. en
heal.publisher ELSEVIER SCI LTD en
heal.journalName Pattern Recognition en
dc.identifier.doi 10.1016/j.patcog.2009.05.007 en
dc.identifier.isi ISI:000270261500030 en
dc.identifier.volume 43 en
dc.identifier.issue 1 en
dc.identifier.spage 369 en
dc.identifier.epage 377 en


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