13. A method for recognizing a handwriting, comprising the steps of: inputting one or more feature vectors representing dynamic stroke information; converting the one or more feature vectors into an N.times.M representation of the one or more feature vectors; counting occurrences of individual ones of a plurality of primitive features within the N.times.M representation; generating, in response to the counted occurrences, at least one first list comprised of one or more symbols that the N.times.M bit-mapped image may represent; generating, in response to the feature vectors, a second list comprised of one or more symbols that the feature vectors may represent; and generating a third list from the at least one first list and the second list, the third list including at least one most probable symbol that the feature vectors may represent.
13. A method for recognizing a handwriting, comprising the steps of: inputting one or more feature vectors representing dynamic stroke information; converting the one or more feature vectors into an N.times.M representation of the one or more feature vectors; counting occurrences of individual ones of a plurality of primitive features within the N.times.M representation; generating, in response to the counted occurrences, at least one first list comprised of one or more symbols that the N.times.M bit-mapped image may represent; generating, in response to the feature vectors, a second list comprised of one or more symbols that the feature vectors may represent; and generating a third list from the at least one first list and the second list, the third list including at least one most probable symbol that the feature vectors may represent. 17. A method as set forth in claim 13 wherein the step of counting occurrences includes the steps of: partitioning the N.times.M bit-mapped image into a plurality of slices; applying an ixj mask over the N.times.M bit-mapped image to locate instances of primitive features; and counting the located instances within each of the plurality of slices.