Share Email Print

Proceedings Paper

Nonlinear and non-Gaussian Bayesian based handwriting beautification
Author(s): Cao Shi; Jianguo Xiao; Canhui Xu; Wenhua Jia
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

A framework is proposed in this paper to effectively and efficiently beautify handwriting by means of a novel nonlinear and non-Gaussian Bayesian algorithm. In the proposed framework, format and size of handwriting image are firstly normalized, and then typeface in computer system is applied to optimize vision effect of handwriting. The Bayesian statistics is exploited to characterize the handwriting beautification process as a Bayesian dynamic model. The model parameters to translate, rotate and scale typeface in computer system are controlled by state equation, and the matching optimization between handwriting and transformed typeface is employed by measurement equation. Finally, the new typeface, which is transformed from the original one and gains the best nonlinear and non-Gaussian optimization, is the beautification result of handwriting. Experimental results demonstrate the proposed framework provides a creative handwriting beautification methodology to improve visual acceptance.

Paper Details

Date Published: 7 March 2014
PDF: 7 pages
Proc. SPIE 9020, Computational Imaging XII, 902012 (7 March 2014); doi: 10.1117/12.2040160
Show Author Affiliations
Cao Shi, Peking Univ. (China)
Qingdao Univ. of Sciece and Technology (China)
Jianguo Xiao, Peking Univ. (China)
Canhui Xu, Peking Univ. (China)
Qingdao Univ. of Science and Technology (China)
Peking Univ. Founder Group and Zhongguancun Haidian Science Park (China)
Wenhua Jia, Peking Univ. (China)

Published in SPIE Proceedings Vol. 9020:
Computational Imaging XII
Charles A. Bouman; Ken D. Sauer, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?