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Proceedings Paper

Personal handwriting identification based on PCA
Author(s): Long Zuo; Yunhong Wang; Tieniu Tan
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Paper Abstract

In this paper, a novel algorithm is presented for writer identification from handwritings. Principal Component Analysis is applied to the gray-scale handwriting images to find a set of individual words which best characterize a person's handwriting style and have maximal difference from other people style. During identification, we only need to utilize a set of individual characteristic words for comparison, instead of comparing the whole handwriting text to identify the writers. So not only is a very high average identification performance of 97.5% obtained, but also a very fast identification speed is achieved in our method. In the experiment, 400 pages ofhandwriting texts, containing almost 16000 Chinese words written by 40 different writers are used to validate the performance ofthe method.

Paper Details

Date Published: 31 July 2002
PDF: 6 pages
Proc. SPIE 4875, Second International Conference on Image and Graphics, (31 July 2002); doi: 10.1117/12.477067
Show Author Affiliations
Long Zuo, Shenyang Institute of Automation (China)
Yunhong Wang, Shenyang Institute of Automation (China)
Tieniu Tan, Sheynang Institute of Automation (China)


Published in SPIE Proceedings Vol. 4875:
Second International Conference on Image and Graphics
Wei Sui, Editor(s)

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