Writer dependent recognition of on-line unconstrained handwriting

Abstract

In this paper, we present a framework for adapting a writer independent system to a user from samples of the user's writing. The writer independent system is modeled using hidden Markov models. Training for a writer involves recomputing the topology and parameters of the hidden Markov models using the writer's data. The framework uses the writer independent system to get an initial alignment of the writer's data. The system described reduces the error rate by an average of 65%. For the results presented, no language model was used.