Calligraphy Character Detection Based on Deep Convolutional Neural Network

Abstract

Calligraphy (the special art of drawing characters with a brush specially made by the Chinese) is an integral part of Chinese culture, and detecting Chinese calligraphy characters is highly significant. At present, there are still some challenges in the detection of ancient calligraphy. In this paper, we are interested in the calligraphy character detection problem focusing on the calligraphy character boundary. We chose High-Resolution Net (HRNet) as the calligraphy character feature extraction backbone network to learn reliable high-resolution representations. Then, we used the scale prediction branch and the spatial information prediction branch to detect the calligraphy character region and categorize the calligraphy character and its boundaries. We used the channel attention mechanism and the feature fusion method to improve the detection effectiveness in this process. Finally, we pre-trained with a self-generated calligraphy database and fine-tuned with a real calligraphy database. We set up two groups of ablation studies for comparison, and the comparison results proved the superiority of our method. This paper found that the classification of characters and character boundaries has a certain auxiliary effect on single character detection.