Categorization of digital ink elements using spectral features

Desriptors for digital ink are usually sequences of features that evolve with time. Since handwriting is an oscillatory process, it is reasonable to think that there is much information in the frequency spectra of such signals. In particular, ink elements of different nature, like text and graphics, might present very different spectra due to different gestural behaviours needed to draw them. Therefore, the descriptor we propose is the Fourier transform of the angle difference between successive ink segments. On a database containing text and symbols, an unsupervised clustering is performed based on this descriptor and clear clusters corresponding to text-only and graphic-only elements emerge