4. A method as in claim 3 further including the steps of: characterizing each of said larger clusters as handwriting prototypes; and providing a handwriting recognizer based on said statistics generated from said prototypes.
4. A method as in claim 3 further including the steps of: characterizing each of said larger clusters as handwriting prototypes; and providing a handwriting recognizer based on said statistics generated from said prototypes. 5. A method as in claim 4 further including the steps of: providing a Hidden Markov Model-based handwriting recognizer wherein one Hidden Markov Model models each of said handwriting prototypes; and creating a network of said Hidden Markov Model for said particular character.