Improvements in HMM Adaptation for Handwriting Recognition Using Writer Identification and Duration Adaptation

This paper presents two techniques for improving adaptation of hidden Markov models (HMMs) for offline handwriting recognition. The first technique uses a novel writer identification algorithm to select training data for adapting writer-dependent models. This helps us get enough annotated samples for adaptation when the writers of test samples are known to have written some manuscripts in the training set. The second technique adapts the transition probabilities of the HMM using estimated mean of model durations from the initial decoding. Experimental results show significant improvements over the standard unsupervised parameter adaptation in our handwriting recognition system.