On-line handwriting analysis using wavelets

Speech and Handwriting interfaces to computing devices have received increased attention recently as alternate human-computer media. Automatic recognition of unconstrained handwritten text must be provided as a capability in handwriting computer interfaces. Variation in writing styles of a single writer at different times and between multiple writers makes unconstrained on-line handwriting recognition a challenging task. On-line handwriting is recorded as a sequence of coordinates as the writer's pen moves along the recording device. Isolated character and word recognition have been addressed by several researchers. More recently, attention has been focussed on the recognition of unconstrained text streams. The dynamic changes in handwriting styles observed in everyday use requires development of methods that are adaptive to local variations. We present a novel application of wavelet-based analysis of pen position, velocity and acceleration time-sequences for segmentation and recognition of text components.