A Rapidly Learning Interpretation System for On-line Cursive Handwriting

This paper describes an interpretation system for on-line cursive handwriting that requires very little initial training and that rapidly learns, and thus adapts to, the handwriting style of a user. Key features are a shape analysis algorithm that eeciently determines shapes in the handwritten word, a linear segmentation algorithm that optimally matches characters identiied in the handwritten word to characters of candidate words, and a learning algorithm that adds, adjusts, or replaces character templates to adapt to the user writing style. In tests, the system was trained on four samples of each character of the alphabet. These samples were written in isolation by one writer. Using a lexicon with 10,000 words, the system achieved for four additional writers an average recognition rate of 81.3% for top choice and 91.7% for the top three choices. The average response time of the system was 1.2 seconds per handwritten word on a SUN SPARC 10 (42 mips).