초록 ▼

A system and method for processing stroke-based handwriting data for the purposes of automatically scoring and clustering the handwritten data to form letter prototypes. The present invention includes a method for processing digitized stroke-based handwriting data of known character strings, where e

A system and method for processing stroke-based handwriting data for the purposes of automatically scoring and clustering the handwritten data to form letter prototypes. The present invention includes a method for processing digitized stroke-based handwriting data of known character strings, where each of the character strings is represented by a plurality of mathematical feature vectors. In this method, each one of the plurality of feature vectors is labelled as corresponding to a particular character in the character strings. A trajectory is then formed for each one of the plurality of feature vectors labelled as corresponding to a particular character. After the trajectories are formed, a distance value is calculated for each pair of trajectories corresponding to the particular character using dynamic time warping method. The trajectories which are within a sufficiently small distance of each other are grouped to form a plurality of clusters. The clusters are used to define handwriting prototypes which identify subcategories of the character.

대표청구항 ▼

[ What is claimed is:] [1.] A method for processing digitized stroke-based handwriting data of known character strings, each segment of said known character strings being represented by a feature vector, said method comprising the steps of:determining a trajectory of said feature vectors in each of

[ What is claimed is:] [1.] A method for processing digitized stroke-based handwriting data of known character strings, each segment of said known character strings being represented by a feature vector, said method comprising the steps of:determining a trajectory of said feature vectors in each of said known character strings corresponding to a particular character, an ith one of said trajectories T.sub.i having n of said feature vectors, T.sub.i ={P.sub.1.sup.i,P.sub.2.sup.i, . . . P.sub.n.sup.i }, and a jth one of said trajectories T.sub.j having m of said feature vectors, T.sub.j ={P.sub.1.sup.j, P.sub.2.sup.j, . . . P.sub.m.sup.j };determining a separation distance d.sub.i,j between each pair of said trajectories T.sub.i and T.sub.j byforming a distance matrix D.sub.i,j where a (k,l) entry D.sub.i,j (k,l) of said distance matrix D.sub.i,j is equal to a distance between P.sub.k.sup.i, a kth one of said feature vectors of said trajectory T.sub.i, and P.sub.l.sup.j, an lth one of said feature vectors of said trajectory T.sub.j ;determining an entry-to-entry path in said distance matrix D.sub.i,j from D.sub.i,j (1,1) to D.sub.i,j (n,m) such that a sum of entries along said entry-to-entry path is a minimum, and setting said sum equal to said separation distance d.sub.i,j ; andgrouping said trajectories into clusters, such that said separation distance of a first pair of said trajectories in a first cluster is smaller than said separation distance of a second pair of said trajectories, said trajectories of said second pair being in different ones of said clusters.