A probabilistic model for cursive handwriting recognition using spatial context

In this work we introduce a probabilistic model that utilizes spatial contextual information to aid recognition when dealing with ambiguous segmentations of handwritten patterns. The recognition problem is formulated as an optimization problem in a Bayesian framework by explicitly conditioning on the spatial configuration of the letters. As a consequence, and in contrast to HMMs, the proposed model can handle duration modeling without an increase in computational complexity. We test the model on a real-world handwriting dataset and discuss several factors that affect the recognition performance.