Automated off-line writer verification using short sentences and grid features

dc.contributor.author	Τσέλιος, Κωνσταντίνος	el
dc.contributor.author	Ζώης, Ηλίας Ν.	el
dc.contributor.author	Νασιόπουλος, Αθανάσιος Α.	el
dc.contributor.author	Καραμπέτσος, Σωτήριος Χ.	el
dc.date.accessioned	2015-01-12T16:40:21Z
dc.date.issued	2015-01-12
dc.identifier.uri	http://hdl.handle.net/11400/3853
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.subject	Writer Verification
dc.subject	Grid Features
dc.subject	Επαλήθευση συγγραφέα
dc.subject	Χαρακτηριστικά πλέγματος
dc.title	Automated off-line writer verification using short sentences and grid features	en
heal.type	conferenceItem
heal.generalDescription	Proceedings of the 1st International Workshop on Automated Forensic Handwriting Analysis (AFHA) 2011	en
heal.classification	Electrical engineering
heal.classification	Electronics
heal.classification	Ηλεκτρολογική μηχανική
heal.classification	Ηλεκτρονική
heal.classificationURI	http://skos.um.es/unescothes/C01311
heal.classificationURI	http://zbw.eu/stw/descriptor/10455-2
heal.classificationURI	N/A-Ηλεκτρολογική μηχανική
heal.classificationURI	N/A-Ηλεκτρονική
heal.dateAvailable	10000-01-01
heal.language	en
heal.access	forever
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Ηλεκτρονικών Μηχανικών Τ.Ε.	el
heal.publicationDate	2011
heal.bibliographicCitation	Tselios, K., Zois, E., Nassiopoulos, A. and karabetsos, S. (2011). Automated off-line writer verification using short sentences and grid features. In the 1st International Workshop on Automated Forensic Handwriting Analysis.	en
heal.abstract	This work presents a feature extraction method for writer verification based on their handwriting. Motivation for this work comes from the need of enchancing modern eras security applications, mainly focused towards real or near to real time processing, by implementing methods similar to those used in signature verification. In this context, we have employed a full sentence written in two languages with stable and predefined content. The novelty of this paper focuses to the feature extraction algorithm which models the connected pixel distribution along predetermined curvature and line paths of a handwritten image. The efficiency of the proposed method is evaluated with a combination of a first stage similarity score and a continuous SVM output distribution. The experimental benchmarking of the new method along with others, state of the art techniques found in the literature, relies on the ROC curves and the Equal Error Rate estimation. The produced results support a first hand proof of concept that our proposed feature extraction method has a powerful discriminative nature.	en
heal.publisher	[χ.ό.]	el
heal.fullTextAvailability	true
heal.conferenceName	International Workshop on Automated Forensic Handwriting Analysis	en
heal.conferenceItemType	full paper