Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICDAR.2011.207
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dc.titleA gradient vector flow-based method for video character segmentation
dc.contributor.authorPhan, T.Q.
dc.contributor.authorShivakumara, P.
dc.contributor.authorSu, B.
dc.contributor.authorTan, C.L.
dc.date.accessioned2013-07-04T08:37:01Z
dc.date.available2013-07-04T08:37:01Z
dc.date.issued2011
dc.identifier.citationPhan, T.Q., Shivakumara, P., Su, B., Tan, C.L. (2011). A gradient vector flow-based method for video character segmentation. Proceedings of the International Conference on Document Analysis and Recognition, ICDAR : 1024-1028. ScholarBank@NUS Repository. https://doi.org/10.1109/ICDAR.2011.207
dc.identifier.isbn9780769545202
dc.identifier.issn15205363
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/41837
dc.description.abstractIn this paper, we propose a method based on gradient vector flow for video character segmentation. By formulating character segmentation as a minimum cost path finding problem, the proposed method allows curved segmentation paths and thus it is able to segment overlapping characters and touching characters due to low contrast and complex background. Gradient vector flow is used in a new way to identify candidate cut pixels. A two-pass path finding algorithm is then applied where the forward direction helps to locate potential cuts and the backward direction serves to remove the false cuts, i.e. those that go through the characters, while retaining the true cuts. Experimental results show that the proposed method outperforms an existing method on multi-oriented English and Chinese video text lines. The proposed method also helps to improve binarization results, which lead to a better character recognition rate. © 2011 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/ICDAR.2011.207
dc.sourceScopus
dc.subjectCurved segmentation path
dc.subjectGradient vector flow
dc.subjectMinimum cost path finding
dc.subjectVideo character segmentation
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1109/ICDAR.2011.207
dc.description.sourcetitleProceedings of the International Conference on Document Analysis and Recognition, ICDAR
dc.description.page1024-1028
dc.identifier.isiut000343450700201
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