Character recognition in natural scene images is a fundamental prerequisite for many text-based image analysis tasks. Generally, local image features are employed widely to recognize characters segmented from natural scene images. In this paper, a curvature-based global image feature and description for segmented character recognition is proposed. This feature is entirely dependent on the curvature information of the image pixels. The proposed feature is employed for segmented character recognition using Chars74k dataset and ICDAR 2003 character recognition dataset. From the two datasets, 1068 and 540 images of characters, respectively, are randomly chosen and 573-dimensional feature vector is synthesized per image. Quadratic, linear and cubic support vector machines are trained to examine the performance of the proposed feature. The proposed global feature and two well-known local feature descriptors called scale invariant feature transform (SIFT) and histogram of oriented gradients (HOG) are compared in terms of classification accuracy, computation time, classifier prediction and training time. Experimental results indicate that the proposed feature yielded higher classification accuracy (%65.3) than SIFT (%53), performed better than HOG and SIFT in terms of classifier training time, and achieved better prediction speed than HOG and less computational time than SIFT.