A Learning-based Approach to Cursive Handwriting Synthesis?

This paper proposes a learning-based approach to synthesize cursive handwriting of the user's personal handwriting style, by combining shape models and physical mod- els together. In the training process, some sample paragraphs written by the user are collected and these cursive handwriting samples are segmented into individual char- acters by using a two-level writer-independent segmentation algorithm. Samples for each letter are then aligned and trained using Principal Component Analysis(PCA). In the synthesis process, a delta log-normal model based conditional sampling algo- rithm is proposed to produce smooth and natural cursive handwriting of the user's style from models.