One stroke cursive character recognition using combination of directional and positional features

The paper proposes a new hybrid approach of directional and positional features for on-line one stroke cursive character recognition based on a dynamic time warping (DTW) algorithm. In our camera based user interface, a user inputs various kinds of characters, including Chinese characters, by moving a fingertip. All strokes of the character are connected, so our recognizer is designed for one stroke cursive character recognition. A quadratic curve equation for a local distance measure is employed in DTW to improve the robustness of the classifier, especially for complicated characters. By reconstructing a positional feature from a directional feature, only directional vectors need to be recorded. Thus, the template file size can be reduced a lot. As the template size is small (about 300 K including Chinese characters) and the templates can be easily customized by a user, the recognizer is suitable for hand-held devices. The efficiency of our approach is demonstrated by the promising experimental results.