1. A method for recognizing unconstrained cursive handwritten words, comprising: processing an image of a handwritten word of one or more characters, the processing step including segmenting the imaged word into a set of one or more segments and determining a sequence of segments using an over-segmentation-relabeling algorithm; extracting feature information of one segment or a combination of several consecutive segments; repeating said extracting step until feature information from segments or combinations thereof have been extracted; and classifying the imaged word as having a string of one or more characters using the extracted feature information, wherein the segmenting the imaged word includes locating a first segment and a last segment in the imaged word, and 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.