Initialization of hidden Markov models for unconstrained on-line handwriting recognition

Abstract

In a hidden Markov model system, the initialization of the model parameters is critical to the performance of the model after retraining. This paper proposes a number of new approaches to the problem of initialization, and demonstrates that a method of smooth alignment results in the best performance.