Chinese calligraphic style representation for recognition

Chinese calligraphy draws a lot of attention for its beauty and elegance. The various styles of calligraphic characters make calligraphy even more charming. But it is not always easy to recognize the calligraphic style correctly, especially for beginners. In this paper, an automatic character styles representation for recognition method is proposed. Three kinds of features are extracted to represent the calligraphic characters. Two of them are typical hand-designed features: the global feature, GIST and the local feature, scale invariant feature transform. The left one is deep feature which is extracted by a deep convolutional neural network (CNN). The state-of-the-art classifier modified quadratic discriminant function was employed to perform recognition. We evaluated our method on two calligraphic character datasets, the unconstraint real-world calligraphic character dataset (CCD) and SCL (the standard calligraphic character library). And we also compare MQDF with other two classifiers, support vector machine and neural network, to perform recognition. In our experiments, all three kinds of feature are evaluated with all three classifiers, respectively, finding that deep feature is the best feature for calligraphic style recognition. We also fine-tune the deep CNN (alex-net) in Krizhevsky et al. (Advances in Neural Information Processing Systems, pp. 1097–1105, 2012) to perform calligraphic style recognition. It turns out our method achieves about equal accuracy comparing with the fine-tuned alex-net but with much less training time. Furthermore, the algorithm style discrimination evaluation is developed to evaluate the discriminative style quantitatively.