20. A method for training an unconstrained cursive character handwritten word recognition system, comprising: processing a corpus of handwritten word images, each imaged word having one or more characters, the processing step including segmenting each of the imaged words into a set of one or more segments and determining a sequence of the segments using an over-segmentation-relabeling algorithm; extracting feature information of individual characters of the imaged words; estimating symbol probability parameters associated with each distinct character so as to allow a statistical measure that given feature information is indicative of a distinct character; and estimating state duration probabilities associated with each distinct character, wherein a state duration probability of a given distinct character represents a probability that a segmented image of the given character will have a duration of a defined number of segments, wherein the segmenting each imaged word includes locating a first segment and a last segment in the imaged word, wherein the determining a sequence of segments using an over-segmentation-relabeling algorithm includes: characterizing segments as either situated segments or unsituated segments, wherein situated segments include the first and last segments, segments having an X-coordinate or Y-coordinate coverage that exceed a threshold value, and small segments that are cursively connected to segments on each side, and wherein unsituated segments are segments not characterized as situated segments; and placing each unsituated segment having a situated segment above or below so as to either immediately precede or follow the situated segment in the sequence of segments:
20. A method for training an unconstrained cursive character handwritten word recognition system, comprising: processing a corpus of handwritten word images, each imaged word having one or more characters, the processing step including segmenting each of the imaged words into a set of one or more segments and determining a sequence of the segments using an over-segmentation-relabeling algorithm; extracting feature information of individual characters of the imaged words; estimating symbol probability parameters associated with each distinct character so as to allow a statistical measure that given feature information is indicative of a distinct character; and estimating state duration probabilities associated with each distinct character, wherein a state duration probability of a given distinct character represents a probability that a segmented image of the given character will have a duration of a defined number of segments, wherein the segmenting each imaged word includes locating a first segment and a last segment in the imaged word, wherein the determining a sequence of segments using an over-segmentation-relabeling algorithm includes: characterizing segments as either situated segments or unsituated segments, wherein situated segments include the first and last segments, segments having an X-coordinate or Y-coordinate coverage that exceed a threshold value, and small segments that are cursively connected to segments on each side, and wherein unsituated segments are segments not characterized as situated segments; and placing each unsituated segment having a situated segment above or below so as to either immediately precede or follow the situated segment in the sequence of segments: 21. The method of claim 20 , wherein the estimating symbol probability parameters step includes calculating representative feature information for each distinct character based on the feature information extracted from character images of a like distinct character in the imaged words.