Chinese Character Calligraphy Teaching System Based on Swin Transformer and CNN
In response to the growing demand for Chinese calligraphy learning,a model combining the Swin Transformer(ST)and Convolutional Neural Network(CNN)was proposed for handwritten Chinese char-acter recognition,subsequently leading to the development of a Chinese character calligraphy teaching sys-tem.The system employed an ST-CNN model for handwriting recognition and classification.The experi-mental results show that the recognition accuracy of the proposed ST-CNN model is around 91.6%,which has a 0.5 percentage points improvement over the traditional ST model.Moreover,the convergence speed of ST-CNN has been improved by about 10 and 30 percentage points respectively compared with traditional CNN and ST models.The developed calligraphy teaching system demonstrates good stability and perform-ance.
deep learningswin transformer modelCNNhandwritten Chinese character recognition
国家科技期刊平台
NSTL
万方数据
维普
