Recognition-based Segmentation of On-Line Cursive Handwriting
Nicholas S. Flann · 1993
flannGnick.cs.usu.edu This paper introduces a new recognition-based segmentation ap-proach to recognizing on-line cursive handwriting from a database of 10,000 English words. The original input stream of z, y pen coor-dinates is encoded as a sequence of uniform stroke descriptions that are processed by six feed-forward neural-networks, each designed to recognize letters of different sizes. Words are then recognized by performing best-first search over the space of all possible segmen-tations. Results demonstrate that the method is effective at both writer dependent recognition (1.7 % to 15.5 % error rate) and writer independent recognition (5.2 % to 31.1 % error rate). 1