Automatic writer identification framework for online handwritten documents using character prototypes

Guo Xian Tan (1) , Christian Viard-Gaudin (1) , A. C. Kot (2)

1 IRCCyN - Institut de Recherche en Communications et en Cybernétique de Nantes
2 Nanyang Technological University [Singapour]

This paper proposes an automatic text-independent writer identification framework that integrates an industrial handwriting recognition system, which is used to perform an automatic segmentation of an online handwritten document at the character level. Subsequently, a fuzzy c-means approach is adopted to estimate statistical distributions of character prototypes on an alphabet basis. These distributions model the unique handwriting styles of the writers. The proposed system attained an accuracy of 99.2% when retrieved from a database of 120 writers. The only limitation is that a minimum length of text needs to be present in the document in order for sufficient accuracy to be achieved. We have found that this minimum length of text is about 160 characters or approximately equivalent to 3 lines of text. In addition, the discriminative power of different alphabets on the accuracy is also reported.

Mots clés: Writer identification; Information retrieval; Online handwriting; Fuzzy c-means; Allographs

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du texte et du document; Traitement du signal et de l'image [eess.SP]

Guo Xian Tan, Christian Viard-Gaudin, A. C. Kot. Automatic writer identification framework for online handwritten documents using character prototypes. Pattern Recognition, 2009, 42, pp.3313-3323. ⟨10.1016/j.patcog.2008.12.019⟩. ⟨hal-00419034⟩