Cursive Script Recognition by Elastic Matching

Abstract

Dynamic programming has been found useful for performing nonlinear time warping for matching patterns in automatic speech recognition. Here, this technique is applied to the problem of recognizing cursive script. The parameters used in the matching are derived from time sequences of x-y coordinate data of words handwritten on an electronic tablet. Chosen for their properties of invariance with respect to size and translation of the writing, these parameters are found particularly suitable for the elastic matching technique. A salient feature of the recognition system is the establishment, in a training procedure, of prototypes by each writer using the system. In this manner, the system is tailored to the user. Processing is performed on a word-by-word basis after the writing is separated into words. Using prototypes for each letter, the matching procedure allows any letter to follow any letter and finds the letter sequence which best fits the unknown word. A major advantage of this procedure is that it combines letter segmentation and recognition in one operation by, in essence, evaluating recognition at all possible segmentations, thus avoiding the usual segmentation-then-recognition philosophy. Results on cursive writing are presented where the alphabet is restricted to the lower-case letters. Letter recognition accuracy is over 95 percent for each of three writers.