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dc.contributor.author | Chawki Djeddi | |
dc.contributor.author | Labiba Souici Meslati | |
dc.contributor.author | Imran Siddiqi | |
dc.contributor.author | Abdelllatif Ennaji | |
dc.contributor.author | Haikal El Abed | |
dc.contributor.author | Abdeljalil Gattal | |
dc.date.accessioned | 2018-01-03T13:15:58Z | |
dc.date.available | 2018-01-03T13:15:58Z | |
dc.date.issued | 2014 | |
dc.identifier.uri | http://hdl.handle.net/123456789/5218 | |
dc.description.abstract | Biometric identification of persons has mainly been based on fingerprints, face, iris and other similar attributes. We propose a handwriting-based biometric identification system using a large database of Arabic handwritten documents. The system first extracts, from each handwritten sample, a set of features including run lengths, edge-hinge and edge-direction features. These features are used by a Multiclass SVM (Support Vector Machine) classifier. Experiments are conducted on a new large database of Arabic handwritings contributed by 1000 writers. The highest identification rate achieved by the combination of run-length and edge-hinge features stands at 84.10%. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Bahria University Islamabad Campus | en_US |
dc.subject | Department of Computer Science CS | en_US |
dc.title | Evaluation of Texture Features for Offline Arabic Writer Identification | en_US |
dc.type | Article | en_US |