Writer adaptation of a HMM handwriting recognition system

Abstract

This paper describes a scheme to adapt the parameters of a tied-mixture, hidden Markov model, on-line handwriting recognition system to improve performance on new writers' handwriting. The means and variances of the distributions are adapted using the Maximum Likelihood Linear Regression technique. Experiments are performed with a number of new writers in both supervised and unsupervised modes. Adaptation on data quantities as small as 5 words is found to result in models with 6% lower error rate than the writer independent model.