Writer dependent recognition of on-line unconstrained handwriting

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