Recognition of Cursive Roman Handwriting - Past, Present and Future
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Generation of Handwritten Characters with Bayesian network based On-line Handwriting Recognizers
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Style-preserving English handwriting synthesis
Pattern Recognition
Biometric technologies and applications
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Retrieval of online handwriting by synthesis and matching
Pattern Recognition
Analysis and modeling of naturalness in handwritten characters
IEEE Transactions on Neural Networks
A novel and robust algorithm to model handwriting skill for haptic applications
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Synthetic on-line signature generation. Part I: Methodology and algorithms
Pattern Recognition
A novel hand reconstruction approach and its application to vulnerability assessment
Information Sciences: an International Journal
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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 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 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.