Handwritten character membership function estimation for word recognition

Traditional hand-written word recognition approaches use character recognition algorithms in the first stage that return confidences in each character under consideration for each segment in the hand-written word. These confidences are used in the second stage to compute a match score between the segmented hand-written word and a given character string. We explore an alternative approach where we view the first stage as a membership assignment process, rather than a character recognition process. The memberships are assigned based on the notion of typicality, and are not relative as in the case of a probabilistic framework. To generate the membership values, we use a robust clustering algorithm that can determine the number of prototypes required to model each character class in a robust and parsimonious manner. Each prototype represents a subclass of the character class. Our experimental results show that when used in conjunction with the proposed approach to word recognition, the memberships generated in this manner produce word recognition results that compare favorably with those of other methods.