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Article dans une revue
Local gradient full-scale transform patterns based off-line text-independent writer identification
Abstract : Handwriting based writer identification is one of the reliable components of behavioral biometrics. A huge effort has been done in recent years to improve the writer identification performance. Our paper presents a new and effective off-line text-independent system for writer identification. Extracting features from handwriting substantially impacts the ability of the classification process to identify the query writers. With the use of suitable classifier, a well-designed and discriminative feature extraction improves the classification performance. For that, we introduce a discriminative yet simple feature method, referred to as Local gradient full-Scale Transform Patterns (LSTP). The proposed LSTP algorithm captures salient local writing structure at small regions of interest of the writing. These writing regions are termed as connected components. In the classification stage, we perform Hamming distance based NN classifier to compare and match LSTP feature vectors. The proposed framework is evaluated on 9 well-known handwritten benchmarks. Experimental results show high identification performance against the current state-of-the-art. (C) 2020 Elsevier B.V. All rights reserved.
https://hal-univ-bourgogne.archives-ouvertes.fr/hal-02877903
Contributeur : Ciad - Université de Bourgogne Connectez-vous pour contacter le contributeur
Soumis le : lundi 22 juin 2020 - 17:54:45
Dernière modification le : mardi 23 juin 2020 - 03:05:18
Contributeur : Ciad - Université de Bourgogne Connectez-vous pour contacter le contributeur
Soumis le : lundi 22 juin 2020 - 17:54:45
Dernière modification le : mardi 23 juin 2020 - 03:05:18