ShufaNet: Classification method for calligraphers who have reached the professional level

TL;DR

ShufaNet is a novel deep learning approach that classifies Chinese calligraphers' styles with high accuracy in few-shot scenarios, using specialized network architecture and prior knowledge.

Contribution

The paper introduces a new network architecture, ShufaNet, with unique loss and attention modules tailored for calligraphy style classification in few-shot learning.

Findings

Achieved 65% accuracy in few-shot classification

Surpassed traditional CNNs like ResNet in accuracy

Outperformed calligraphy students in classification tasks

Abstract

The authenticity of calligraphy is significant but difficult task in the realm of art, where the key problem is the few-shot classification of calligraphy. We propose a novel method, ShufaNet ("Shufa" is the pinyin of Chinese calligraphy), to classify Chinese calligraphers' styles based on metric learning in the case of few-shot, whose classification accuracy exceeds the level of students majoring in calligraphy. We present a new network architecture, including the unique expression of the style of handwriting fonts called ShufaLoss and the calligraphy category information as prior knowledge. Meanwhile, we modify the spatial attention module and create ShufaAttention for handwriting fonts based on the traditional Chinese nine Palace thought. For the training of the model, we build a calligraphers' data set. Our method achieved 65% accuracy rate in our data set for few-shot learning, surpassing resNet and other mainstream CNNs. Meanwhile, we conducted battle for calligraphy major students, and finally surpassed them. This is the first attempt of deep learning in the field of calligrapher classification, and we expect to provide ideas for subsequent research.