A stochastic nearest neighbor character prototype approach for online writer identification

One novel technique for identifying the writer of an online handwritten document is proposed. This technique makes use of a character prototype distribution to model the specific allographs used by a given writer. In this paper, we propose to extend and improve upon this newly established methodology by S.K. Chan et al (2008) by making use of a stochastic nearest neighbor algorithm to estimate the character prototype distribution. The proposed method is text independent and relies on the automatic segmentation of the handwritten text at the character level. Our results show that this approach attained a writer identification rate of 99.2% when a reference database of 120 writers is used. Experiments related to the effect of the length of text of the document on the performance of the writer identification system are also reported.