On-Line Handwritten Alphanumeric Character Recognition Using Feature Sequences

Xiaolin Li, Dit-Yan Yeung

Abstract: In this paper we present an approach in which an on-line handwritten character is characterized by a sequence of dominant points in strokes and a sequence of writing directions between consecutive dominant points. The directional information is used for character preclassification and the positional information is used for fine classification. Doth preclassification and fine classification are based on dynamic programming matching. A recognition experiment has been conducted with 62 character classes of different writing styles and 21 people as data contributors. The recognition rate of this experiment is 91%, with 7.9% substitution rate and 1.1% rejection rate. The average processing time is 0.35 second per character on a 486 50MHz personal computer.