A Ballistic Stroke Representation of Online Handwriting for Recognition

Robust segmentation of ballistic strokes from online handwritten traces is critical in parameter estimation of stroke based models for applications such as recognition, synthesis, and writer identification. In this paper we propose a new method for segmenting ballistic strokes from online handwriting. Traditional methods of ballistic stroke segmentation rely on detection of local minima of pen speed. Unfortunately, this approach is highly sensitive to noise, in sensing and in both spatial and temporal dimensions. We decompose the problem into two steps, where the spatial noise is filtered out in the first step. The ballistic stroke boundaries are then detected at the local curvature maxima, which we show to be invariant to temporal sampling noise. We also propose a bag-of-strokes representation based on ballistic stroke segmentation for online character recognition that improves the state-of-the-art recognition accuracies on multiple datasets.