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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/15941
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dc.contributor.authorMandal R.-
dc.contributor.authorPratim Roy, Partha-
dc.contributor.authorPal U.-
dc.date.accessioned2020-12-02T11:42:03Z-
dc.date.available2020-12-02T11:42:03Z-
dc.date.issued2011-
dc.identifier.citationProceedings of the International Conference on Document Analysis and Recognition, ICDAR, (2011), 1170- 1174. Beijing-
dc.identifier.isbn9.78077E+12-
dc.identifier.issn15205363-
dc.identifier.urihttps://doi.org/10.1109/ICDAR.2011.236-
dc.identifier.urihttp://repository.iitr.ac.in/handle/123456789/15941-
dc.description.abstractAutomatic separation of signatures from a document page involves difficult challenges due to the free-flow nature of handwriting, overlapping/touching of signature parts with printed text, noise, etc. In this paper, we have proposed a novel approach for the segmentation of signatures from machine printed signed documents. The algorithm first locates the signature block in the document using word level feature extraction. Next, the signature strokes that touch or overlap with the printed texts are separated. A stroke level classification is then performed using skeleton analysis to separate the overlapping strokes of printed text from the signature. Gradient based features and Support Vector Machine (SVM) are used in our scheme. Finally, a Conditional Random Field (CRF) model energy minimization concept based on approximated labeling by graph cut is applied to label the strokes as "signature" or "printed text" for accurate segmentation of signatures. Signature segmentation experiment is performed in "tobacco" dataset and we have obtained encouraging results. © 2011 IEEE.-
dc.description.sponsorshipTC10 (Graph. Recogn.) TC11 (Read. Syst.) (IAPR);Chinese Academy of Sciences;NSFC;FUJITSU;Hanvon Technology-
dc.language.isoen_US-
dc.relation.ispartofProceedings of the International Conference on Document Analysis and Recognition, ICDAR-
dc.subjectCRF-
dc.subjectPrinted/handwritten text separation-
dc.subjectSignature segmentation-
dc.subjectSignature verification-
dc.titleSignature segmentation from machine printed documents using conditional random field-
dc.typeConference Paper-
dc.scopusid54410932900-
dc.scopusid56880478500-
dc.scopusid57200742116-
dc.affiliationMandal, R., Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, Kolkata-108, India-
dc.affiliationRoy, P.P., Laboratoire d'Informatique, Université François Rabelais, Tours, France-
dc.affiliationPal, U., Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, Kolkata-108, India-
dc.description.correspondingauthorMandal, R.; Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, Kolkata-108, India; email: ranjumandal@gmail.com-
dc.identifier.conferencedetails11th International Conference on Document Analysis and Recognition, ICDAR 2011, Beijing, 18-21 September 2011-
Appears in Collections:Conference Publications [CS]

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