Recognition of calligraphy style based on global feature descriptor

The study of digital Chinese calligraphy has become more valuable nowadays. While existing researches on Chinese calligraphy analysis are primarily focused on stroke-based character recognition and simulation, we propose a global feature descriptor to deal with style recognition problem in this paper. The proposed method extracts three categories of character features: position features, proportion features and projection features. These features are then used to train an SVM classifier of calligraphy style. We test the global feature based classifier on five-style Chinese calligraphy character set. The experimental results show that the proposed method can achieve good classification accuracy, proving the effectiveness of the global feature descriptor in calligraphy style recognition.