Constructing Calligraphy Evaluation Model Based on Writing Movement with LSTM Network

Calligraphy has a long history as one of the Chinese outstanding traditional arts. However, calligraphy, as an artistic derivative of Chinese characters, suffers from a lack of teachers, a variety of disciplines, and confusing aesthetic standards. With the development of calligraphy, the need for calligraphy evaluation has also gradually increased, but the traditional way to evaluate calligraphy works relies too much on the work of calligraphy and ignores the motion of writing. To address such problems, we construct a multimodal dataset that contains images of calligraphy works, time series data of writing movements and aesthetic evaluation labels. To exploit the time series data of writing movement, we propose a strong benchmark that combines the Long Short-Term Memory network with the K-nearest neighbor algorithm. The proposed model achieved the best accuracy compared with baseline methods. And the evaluation results of our model are close to that of calligraphy experts. This study serves as a guide to the aesthetic evaluation of computational calligraphy.