This paper presents a systematic multi-path HMM topology design algorithm to better model online handwriting of East Asian characters. This data-driven algorithm solves three key problems in HMM topology design. First, HMM path number determination is formalized as a clustering problem using Subsequence Direction Histogram Vector (SDHV) as feature of both writing order and style. Second, Curvature Scale Space-based (CSS-based) substroke segmentation is used to calculate the optimal state number and initial state parameters. Third, Self-rotation restricted corner state and imaginary stroke state are designed to determine state connectivity and Gaussian mixture number in order to achieve better state alignment. Experiments on large character sets demonstrate both a significant relative error reduction rate and high recognition accuracy using the proposed algorithm.