Online Handwritten Stroke Type Determination Using Descriptors Based on Spatially and Temporally Neighboring Strokes

We investigate the task of single-stroke classification into one of three classes (text, figure, or table rule lines). Individual strokes form handwriting structures such as text lines, figures, and tables in combination with peripheral strokes. To classify strokes using local contexts of neighborhood strokes, we propose a composite descriptor that represents in detail the relation between individual strokes and temporal and spatial neighborhood strokes. Evaluation of online handwritten documents written in English and in Japanese indicate that the proposed method more accurately classifies strokes than does the conventional method that employs shape-related features of a single stroke.