Improved dynamic programming-based handwritten word recognition using optimal order statistics

Handwritten word recognition is a difficult problem. In the standard segmentation-based approach to handwritten word recognition, individual character class confidence scores are combined to estimate confidences concerning the various hypothesized identities for a word. The standard combination method is the mean. Previously, we demonstrated that a Choquet integral provided higher recognition rates than the mean [8]. Our previous work with the Choquet integral relied on a restricted class of measures. For this class of measures, operators based on the Choquet integral are equivalent to a subset of a class of operators known as linear combinations of order statistics (LOS). In this paper, we extend our previous work to find the optimal LOS operator for combining character class confidence scores. Experimental results are provided on about 1300 word images.