Unsupervised HMM Adaptation Using Page Style Clustering

In this paper we present an innovative two-stage adaptation approach for handwriting recognition that is based on clustering of similar pages in the training data. In our approach, we first perform page clustering on training data using features such as contour slope, pen pressure, writing velocity, and stroke sparseness. Next, we adapt the writer-independent Hidden Markov models (HMMs) to each cluster in the training data. While decoding a test page, we first determine the cluster the test page belongs to and then decode the page with the model associated with that cluster. Experimental results with the two-stage adaptation show significant gains on a held-out validation set.