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A Stochastic Nearest Neighbor Character Prototype Approach for Online Writer Identification

By Guoxian Tan, Christian Viard-Gaudin and Alex Kot

Abstract

International audienceOne 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 1used by a given writer. In this paper, we propose to extend and improve upon this newly established methodology [1] 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

Topics: [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Publisher: HAL CCSD
Year: 2008
OAI identifier: oai:HAL:hal-00422348v1
Provided by: HAL-Univ-Nantes

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