Writer Identification Using Allograph Distributions. Identification de Scripteurs Utilisant les Distributions d’Allographes

Writer Identification Using Allograph Distributions

Identification de Scripteurs Utilisant les Distributions d’Allographes

Christian Viard-Gaudin Guo Xian Tan  Alex C. Kot 

IRCCyN, UMR CNRS 6597, Ecole Polytechnique de l’Université de Nantes, France

Nanyang Technological University, Singapore

15 December 2009
31 December 2009
| Citation



A method is proposed to allow the retrieval of the identity of the writer of a non-constraint handwritten text by matching it with some reference handwritten documents.The matching is based on a metric computed on the distributions of the allograph of the letters featuring a unique writing style.An automatic system segments the text into characters and assigns a partial membership to the different representative prototypes of the considered letter of the Roman alphabet. Two different datasets are used to assess this system.A writer identification rate of 99.2% is obtained when the reference dataset is composed of 120 French documents.On the other dataset with 200 English texts,the identification rate reaches 87%.Online handwriting is considered by this system.


Ce papier propose une méthode permettant d’identifier le scripteur d’un texte quelconque de quelques lignes en le comparant à des écritures de références. La comparaison est basée sur une mesure de mise en correspondance des distributions des allographes de lettres représentatifs des styles d’écriture. Un système automatique segmente le texte en lettres,puis classe chaque lettre de manière probabiliste parmi les prototypes disponibles pour cette lettre. Deux bases de complexité différentes sont utilisées pour valuer ce système. Un taux d’identification de 99,2 % est obtenu sur une base de recherche de 120 textes écrits en français,tandis qu’il se situe à 87 % sur une base de recherche de 200 textes écrits en anglais. Cette méthode est développée sur de l’écriture en ligne.


Writer identification,information retrieval,online handwriting,k-nearest neighbor,allograph.

Mots clés 

Identification de scripteur,recherche d’information,écriture manuscrite en-ligne,k-plus-proches-voisins, allographe.

2.Les Méthodes Existantes
3.La Méthode Proposée
4.Résultats Expérimentaux

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