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Combining shape and physical modelsfor online cursive handwriting synthesis

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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.

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Author information

Authors and Affiliations

  1. Microsoft Research Asia, Sigma Center, 100080, Zhichun RD, Beijing, China

    Ying-Qing Xu & Heung-Yeung Shum

  2. Department of Electrical Engineering, University of Washington, WA 98195, Seattle, USA

    Jue Wang

  3. Robotics Institute, Carnegie Mellon University, PA 15213, Pittsburgh, USA

    Chenyu Wu

Authors
  1. Jue Wang
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  2. Chenyu Wu
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  3. Ying-Qing Xu
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  4. Heung-Yeung Shum
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Corresponding author

Correspondence to Jue Wang.

Additional information

Received: 26 April 2003, Accepted: 27 September 2004, Published online: 29 November 2004

Correspondence to: Jue Wang

Jue Wang and Chenyu Wu completed this work while interns at Microsoft Research Asia.

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Wang, J., Wu, C., Xu, YQ. et al. Combining shape and physical modelsfor online cursive handwriting synthesis. IJDAR 7, 219–227 (2005). https://doi.org/10.1007/s10032-004-0131-6

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  • DOI: https://doi.org/10.1007/s10032-004-0131-6

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