Character recognition in natural scene images is a fundamental prerequisite for many text-based imageanalysis tasks. Generally, local image features are employed widely to recognize characters segmented from natural sceneimages. In this paper, a curvature-based global image feature and description for segmented character recognition isproposed. This feature is entirely dependent on the curvature information of the image pixels. The proposed feature isemployed 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-dimensionalfeature vector is synthesized per image. Quadratic, linear and cubic support vector machines are trained to examinethe performance of the proposed feature. The proposed global feature and two well-known local feature descriptorscalled scale invariant feature transform (SIFT) and histogram of oriented gradients (HOG) are compared in terms ofclassification accuracy, computation time, classifier prediction and training time. Experimental results indicate that theproposed feature yielded higher classification accuracy (%65.3) than SIFT (%53), performed better than HOG and SIFTin terms of classifier training time, and achieved better prediction speed than HOG and less computational time thanSIFT.