Addressing Class Distribution Issues of the Drawing vs Writing Classification in an Ink Stroke Sequence

Abstract

Complicated by temporal correlations among the strokes and varying distributions of the underlying classes, the drawing/writing classification of ink strokes in a digital ink file poses interesting challenges. In this paper, we present our efforts in addressing some of the issues. First, we describe how we adjust the outputs of the neural network to a priori probabilities of new observations to produce more accurate estimates of the posterior probabilities. Second, we describe how to adapt the parameters of the HMM to new data sets. Albeit the fact that the emission probabilities of the HMM are computed indirectly from the outputs of the neural network, our modified Baum-Welch algorithm still finds the correct estimates for the HMM's parameters. We also present experimental results of our new algorithms on 6 real world data sets. The results show that our methods increase the F Measures of both the drawing and the writing classes on the more ''drawing intensive'' data sets which have stronger temporal correlations. But they do not perform well on the more ''writing intensive'' data sets.