There are many instances in which character shape of a class changes smoothly to that of another class. Although there are many ways of the change, the most delicate change is curvature feature. The paper treat this problem systematically in both theoretically and experimentally. Specifically some confusing pairs were constructed which are well known in the field of OCR, such as 2 Z and 4 9. A series of samples generated using each model which change subtly were provided to conduct a psychological experiment. The results exhibit a monotone change of recognition rates from nearly 100% to 0% as the shape changes continuously. To imitate the humans' performance, feature of curvature was extracted based on continuous function representation based on Bezier's spline curve. Specifically two methods were tried from theoretical and engineering points of view and very successful results were obtained.