SRAFE: Siamese Regression Aesthetic Fusion Evaluation for Chinese Calligraphic Copy

Abstract

Evaluation of calligraphic copy is the core of Chinese calligraphy appreciation and inheritance. However, previous aesthetic evaluation studies often focussed on photos and paintings, with few attempts on Chinese calligraphy. To solve this problem, a Siamese regression aesthetic fusion method is proposed, named SRAFE, for Chinese calligraphy based on the combination of calligraphy aesthetics and deep learning. First, a dataset termed Evaluated Chinese Calligraphy Copies (E3C) is constructed for aesthetic evaluation. Second, 12 hand-crafted aesthetic features based on the shape, structure, and stroke of calligraphy are designed. Then, the Siamese regression network (SRN) is designed to extract the deep aesthetic representation of calligraphy. Finally, the SRAFE method is built by fusing the deep aesthetic features with the hand-crafted aesthetic features. Experimental results show that scores given by SRAFE are similar to the aesthetic evaluation label of E3C, proving the effectiveness of the authors’ method.