Details

Autor(en) / Beteiligte

• Tan, Guo Xian
• Viard-Gaudin, Christian
• Kot, Alex C.

Titel

Automatic writer identification framework for online handwritten documents using character prototypes

Ist Teil von

• Pattern recognition, 2009-12, Vol.42 (12), p.3313-3323

Ort / Verlag

Kidlington: Elsevier Ltd

Erscheinungsjahr

2009

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• 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.