In this paper an integrated approach for modeling, learning and synthesizing personal cursive handwriting is proposed. Cursive handwriting is modeled by a tri-unit handwriting model, which focuses on both the handwritten letters and the interconnection strokes of adjacent letters. Handwriting strokes are formed from generative models that are based on control points and B-spline curves. In the two-step learning process, a template-based matching algorithm and a data congealing algorithm are first proposed to extract training vectors from handwriting samples, and then letter style models and concatenation style models are trained separately. In the synthesis process, isolated letters and ligature strokes are generated from the learned models and concatenated with each other to produce the whole word trajectory, with guidance from a deformable model. Experimental results show that the proposed system can effectively learn the individual style of cursive handwriting and has the ability to generate novel handwriting of the same style.
['Jue Wang', 'Chenyu Wu', 'Ying-Qing Xu', 'Heung-Yeung Shum', 'Liang Ji']
Learning-based cursive handwriting synthesis