Supervised hidden Markov modeling for on-line handwriting recognition

The performance of a large alphabet handwriting recognition system based on a probabilistic framework is critically tied to the quality of the prototype distributions that are established in the relevant feature space. To better account for handwriting variability, we describe a supervised strategy for the construction of prototype distributions which are more robust to allograph deformations. The idea is to incorporate supervision to relate the allographic models to their manifestations in the feature space. This makes for a better utilization of the available training data, while at the same time allowing for a short design time turn around. The performance of this method is illustrated on a discrete handwriting recognition task with an alphabet of 81 characters