Representing and Characterizing Handwritten Mathematical Symbols through Succinct Functional Approximation

We model on-line ink traces for a set of 219 symbols to "best fit" low-degree polynomial series. Using a collection of mathematical writing samples, we find that in many cases this provides a succinct way to model the stylus movements of actual test users. Furthermore, even without further similarity-processing, the polynomial coefficients from the writing samples form clusters which often contain the same character as written by different users. We find this style of characterization to be an attractive tool due to the suitability of the representation to computation and mathematical analysis.