Novel geometric features for off-line writer identification

Al-Maadeed, Somaya, Hassaine, Abdelaali, Bouridane, Ahmed and Tahir, Muhammad (2016) Novel geometric features for off-line writer identification. Pattern Analysis and Applications, 19 (3). pp. 699-708. ISSN 1433-7541

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Official URL: http://dx.doi.org/10.1007/s10044-014-0438-y

Abstract

Writer identification is an important field in forensic document examination. Typically, a writer identification system consists of two main steps: feature extraction and matching and the performance depends significantly on the feature extraction step. In this paper, we propose a set of novel geometrical features that are able to characterize different writers. These features include direction, curvature, and tortuosity. We also propose an improvement of the edge-based directional and chain code-based features. The proposed methods are applicable to Arabic and English handwriting. We have also studied several methods for computing the distance between feature vectors when comparing two writers. Evaluation of the methods is performed using both the IAM handwriting database and the QUWI database for each individual feature reaching Top1 identification rates of 82 and 87 % in those two datasets, respectively. The accuracies achieved by Kernel Discriminant Analysis (KDA) are significantly higher than those observed before feature-level writer identification was implemented. The results demonstrate the effectiveness of the improved versions of both chain-code features and edge-based directional features.

Item Type: Article
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 09 Jan 2015 08:56
Last Modified: 09 May 2017 23:03
URI: http://nrl.northumbria.ac.uk/id/eprint/21092

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