Combining shape and physical modelsfor online cursive handwriting synthesis

Authors:
Jue Wang;Chenyu Wu;Ying-Qing Xu;Heung-Yeung Shum
Affiliations:
Department of Electrical Engineering, University of Washington, WA 98195, Seattle, USA;Robotics Institute, Carnegie Mellon University, PA 15213, Pittsburgh, USA;Microsoft Research Asia, Sigma Center, 100080, Zhichun RD, Beijing, China;Microsoft Research Asia, Sigma Center, 100080, Zhichun RD, Beijing, China
Venue:
International Journal on Document Analysis and Recognition
Year:
2005

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Abstract

This paper proposes a novel learning-based approach to synthesizing cursive handwriting of a user's personal handwriting style by combining shape and physical models. In the training process, some sample paragraphs written by a user are collected and these cursive handwriting samples are segmented into individual characters by using a two-level writer-independent segmentation algorithm. Samples for each letter are then aligned and trained using shape models. In the synthesis process, a delta log-normal model based conditional sampling algorithm is proposed to produce smooth and natural cursive handwriting of the user's style from models.