Abstract
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.
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