Writer and writing-style classification in the recognition of online handwriting

One of the problems in the automatic recognition of cursive and mixed-cursive handwriting is the large variation of handwriting styles in a population. Automatic detection of the generic handwriting style, or identification of the writer could be useful to counteract this problem. The starting point for the writing style analyses is an existing recognition system for online connected-cursive handwriting (Schomaker and Teulings, 1990; Schomaker 1993). The input to this recognizer consists of pen-tip movements produced during the writing of a single word, using equidistant sampling in time. Data are lowpass filtered and normalized on size and slant. In the segmentation stage, strokes are used, which are defined as the pen-tip trajectory between two consecutive minima in the pen-tip velocity. A neural-network technique, the Kohonen self-organizing map, is used to obtain a finite list of prototypical strokes (PS): A stroke alphabet (PSA). This stroke alphabet approximates the handwriting in the training set with a minimized rms error, and can be shown to generalize well to strokes in the handwriting of unknown writers. Thus, up to this stage of processing, the recognition system is writer independent. At the next level of processing, character classification takes place, in which sequences of stroke codes are classified as letters by a probabilistic stroke transition network. It should be noted, that at this level there is a very strong dependence on writer and writing style