| Titre : |
Hybrid features fusion for writer identification usingsingle handwritten words |
| Type de document : |
document électronique |
| Auteurs : |
Rayane Kadem, Auteur ; Yacine Rabehi, Auteur ; Nesrine Bouadjenek, Directeur de thèse |
| Editeur : |
[S.l.] : [s.n.] |
| Année de publication : |
2020 |
| Importance : |
1 fichier PDF (5.6 M) |
| Présentation : |
ill. |
| Note générale : |
Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Électronique : Alger, École Nationale Polytechnique : 2020
Bibliogr. f. 80 - 84 |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Writer identification
Handwriting
MLOG
Hybrid features
CNN |
| Index. décimale : |
PN00720 |
| Résumé : |
Handwriting as a part of behavioral biometrics has been proved to having the abilityto sufficiently differentiate any two individuals. Therefore, in this work, we proposea system for writer identification using single handwritten words. In this regard, wepropose, associated to Support Vector Machine (SVM) classifier, a hybrid features fusionthat combined features extracted from a new descriptor namely, Multiscale Local OrientedGradient (MLOG) and features generated from Convolutional Neural Network VGG-16.Two known approaches of writer identification were addressed: writer-dependent andwriter-independent. Experiments conducted on two standard datasets, showed satisfyingand very promising results |
Hybrid features fusion for writer identification usingsingle handwritten words [document électronique] / Rayane Kadem, Auteur ; Yacine Rabehi, Auteur ; Nesrine Bouadjenek, Directeur de thèse . - [S.l.] : [s.n.], 2020 . - 1 fichier PDF (5.6 M) : ill. Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Électronique : Alger, École Nationale Polytechnique : 2020
Bibliogr. f. 80 - 84 Langues : Anglais ( eng)
| Mots-clés : |
Writer identification
Handwriting
MLOG
Hybrid features
CNN |
| Index. décimale : |
PN00720 |
| Résumé : |
Handwriting as a part of behavioral biometrics has been proved to having the abilityto sufficiently differentiate any two individuals. Therefore, in this work, we proposea system for writer identification using single handwritten words. In this regard, wepropose, associated to Support Vector Machine (SVM) classifier, a hybrid features fusionthat combined features extracted from a new descriptor namely, Multiscale Local OrientedGradient (MLOG) and features generated from Convolutional Neural Network VGG-16.Two known approaches of writer identification were addressed: writer-dependent andwriter-independent. Experiments conducted on two standard datasets, showed satisfyingand very promising results |
|
Ajouter le résultat dans votre panier
Visionner les documents numériques
Faire une suggestion Affiner la rechercheHybrid features fusion for writer identification usingsingle handwritten words (2020)
