Using spiking neural networks to assist fine art and philology study: to classify styles of Chinese calligraphy with minimal computing power

Zheng Luan, Xiangqi Kong, Shuimu Zeng, 姚羽珂, Yaxuan Zhang, Xuerui Qiu

Keywords: Spiking Neural Networks, Chinese Calligraphy Style, Classification, Low Computing Power

TL;DR: A good cross-disciplinary work of Spiking Neural Networks.

Abstract: Spiking Neural Networks have drawn much attention for their potential deployment in low computing power scenarios and interdisciplinary research. This paper focuses on a novel task of classifying Chinese Calligraphy styles properly and introduces a cutting-edge network called CaStySNN. Compared to same-structured traditional artificial neural networks, CaStySNN requires significantly less computing power, while demonstrating superior performances across different datasets. In the future, this innovative approach can be applied to neuromorphic devices, offering solutions to a wide range of challenges in the realms of fine arts and philology.

Submission Number: 76