38. The handwritten word recognition system of claim 36 , wherein the empirically derived mutual character confusion information is derived from a confusion matrix generated from symbol probabilities computed from training the system or by running the system in a recognition mode using character images as input.
36. An unconstrained cursive character handwritten word recognition system, comprising a processor including: an image processing module operable to process an image of a handwritten word of one or more characters, wherein the processing of the imaged word includes segmenting the imaged word into a finite number of segments and determining a sequence of the segments using an over-segmentation-relabeling algorithm, wherein each character includes one or more consecutive segments; a feature extraction module operable to derive a feature vector to represent feature information of one segment or a combination of several consecutive segments; a classification module operable to determine an optimal string of one or more characters as composing the imaged word; and: a post-processing module operable to output hypotheses of one or more words from a given dictionary which are suggested by the optimal string, wherein the hypotheses are based on weighted edit distances using weight factors determined from linguistic reasoning and empirically derived mutual character confusion information and without using probabilities given by a modified Viterbi algorithm as a weight factor.