In this paper, we propose a simple but effective method of modeling hand drawn gestures based on their angles and curvature of the trajectories. Each gesture trajectory is composed of a unique series of straight and curved segments. In our Hidden Markov Model (HMM) implementation, these gestures are modeled as connected series of states analogous to series of phonemes in speech recognition. The novelty of the work presented here is the automated process we developed in segmenting gesture trajectories based on a simple set of threshold values in curvature and accumulated curvature angle. In order to represent its angular distribution of each separated states, the von Mises distribution is used. Likelihood based state segmentation was implemented in addition to the threshold based method to ensure that gesture sets are segmented consistently. The proposed method can separate each angular state of training data at the initialization step, thus providing a solution to mitigate ambiguity on initializing HMM. For comparative studies, the proposed automated state segmentation based HMM initialization was considered over the conventional method. Effectiveness of the proposed method is shown as it achieved higher recognition rates in experiments over conventional methods.