9. The method according to claim 1 wherein the feature points comprise end feature points, corner feature points, and intersection feature points.
1. A method for recognizing a static handwritten word of cursive script, comprising: optically reading said word and forming a bit map of pixels representing said word; skeletonizing said word within said bit map; segmenting said skeletonized word into one or more primitives, said skeletonized word including a plurality of feature points and said primitives each comprising a continuous segment of said skeletonized word extending between an original feature point and a terminal feature point; forming a sequence representing the order in which said primitives were written by ordering said primitives in succession beginning at the left side of said word; and classifying said word by comparing said primitives and said sequence with stored primitives and their associated sequences for known words, wherein the step of forming a sequence comprises: locating a primitive which is left-most in said word, examining said left-most primitive for the presence of one or more of said end points and designating said left-most primitive as a first primitive if it contains one or more of said end points; if said left-most primitive does not contain one or more of said end points, examining a primitive connected with said left-most primitive for the presence of an end point, and designating said connected primitive as said first primitive if it contains an end point and designating said left-most primitive as said first primitive if said connected primitive does not contain an end point; and ordinally designating as subsequent primitives each of said primitives which are connected which are connected with said first primitive and with said subsequent primitives.