7 March 2014 Nonlinear and non-Gaussian Bayesian based handwriting beautification
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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.
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Cao Shi, Jianguo Xiao, Canhui Xu, Wenhua Jia, "Nonlinear and non-Gaussian Bayesian based handwriting beautification", Proc. SPIE 9020, Computational Imaging XII, 902012 (7 March 2014); doi: 10.1117/12.2040160; https://doi.org/10.1117/12.2040160
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