Writer adaptation of a HMM handwriting recognition system

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