Text-independent writer recognition using multi-script handwritten texts

  • Authors:

  • Chawki Djeddi;Imran Siddiqi;Labiba Souici-Meslati;Abdellatif Ennaji

  • Affiliations:

  • LAMIS Laboratory, University of Tebessa, Tebessa, Algeria and Department of Computer Science, Badji Mokhtar-Annaba University, Annaba, Algeria;Department of Computer Science, Bahria University, Islamabad, Pakistan;Department of Computer Science, Badji Mokhtar-Annaba University, Annaba, Algeria;LITIS Laboratory, Rouen University, Rouen, France

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2013

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Abstract

This paper presents a text-independent writer recognition method in a multi-script environment. Handwritten texts in Greek and English are considered in this study. The objective is to recognize the writer of a handwritten text in one script from the samples of the same writer in another script and hence validate the hypothesis that writing style of an individual remains constant across different scripts. Another interesting aspect of our study is the use of short handwritten texts which was implied to resemble the real life scenarios where the forensic experts, in general, find only short pieces of texts to identify a given writer. The proposed method is based on a set of run-length features which are compared with the well-known state-of-the-art features. Classification is carried out using K-Nearest Neighbors (K-NN) and Support Vector Machines (SVM). The experimental results obtained on a database of 126 writers with 4 samples per writer show that the proposed scheme achieves interesting performances on writer identification and verification in a multi-script environment.