A unified approach to on-line cursive script segmentation and feature extraction

A stroke segmentation algorithm based on curvature information processing and scale-space filtering is proposed. The strokes extracted are very close to strokes described by psychophysicists. A simple online author-dependent recognition system that uses the segmentation algorithm as a preprocessing stage is described and its performance evaluated for handwritten data obtained from five authors. The segmentation results indicate a performance much better than that of techniques based on direct estimation of penspeed or curvature. The scale-space plot of stroke boundaries also locates many descriptive features detectable in cursive script, allowing the programmer to choose any desired scale or scales. Once segmentation is performed, the problem of recognizing recursive script is not much more difficult than the problem of recognizing isolated characters. The performance of the recognition system appears to be better than that of any cursive script recognition system designed so far, even though extremely simplifying assumptions have been made in the stroke matching stage