This paper studies the importance of the features that represent the global structure of character strokes to character recognition. Most existing character recognition methods based on character stroke features utilize a set or a sequence of local features such as xy-coordinates and local direction of strokes. This is natural from the viewpoint that each stroke is a trajectory and thus can be represented as a sequence of local features. This viewpoint, however, has a clear limitation in that local features cannot deal with global structure directly. For example, the sequence of local features cannot deal with the fact that the two end points of character "0" should be close to each other. In this paper we propose a simple and novel global feature that describes the global structure of the character shape of each class. We prove the importance of the global feature through a feature selection experiment. Specifically, we show that the global features are more often selected than local features to enhance classification accuracy under the AdaBoost-based machine learning framework. Recognition experiments using online numeral data show also that the use of global features improves recognition accuracy.