Dynamical encoding of cursive handwriting.

A model-based approach to on-line cursive handwriting analysis and recognition is presented and evaluated. In this model, on-line handwriting is considered as a modulation of a simple cycloidal pen motion, described by two coupled oscillations with a constant linear drift along the line of the writing. By slow modulations of the amplitudes and phase lags of the two oscillators, a general pen trajectory can be efficiently encoded. These parameters are then quantized into a small number of values without altering the writing intelligibility. A general procedure for the estimation and quantization of these cycloidal motion parameters for arbitrary handwriting is presented. The result is a discrete motor control representation of the continuous pen motion, via the quantized levels of the model parameters. This motor control representation enables successful word spotting and matching of cursive scripts. Our experiments clearly indicate the potential of this dynamic representation for complete cursive handwriting recognition.