Chinese Character Stroke Extraction Method based on Hierarchical Multi-Model

Abstract Stroke extraction of Chinese characters plays an important role in Chinese character recognition, calligraphy aesthetic quality evaluation and handwriting synthesis. However, most of the existing methods perform well on printed characters, but not well on ancient calligraphy and handwritten characters. In addition, most of these methods only use a single model based on graph theory or deep learning to achieve end-to-end stroke extraction, resulting in limited room for model performance improvement. In this paper, we propose a Chinese character stroke extraction method based on hierarchical multi-model including four steps, which apply KNN clustering algorithm into stroke extraction and improved the performance of stroke extraction based on instance segmentation. Firstly, we use a stacked model driven calligraphic character recognition method to recognize the input character image. Then we use the font recognition model to match the input character image with images with annotated strokes. For those images with matching rate higher than 99\%, stroke extraction method based on KNN pixel clustering is used to extract the strokes. Thirdly, for those images with matching rate less than 99\%, we separate the non-connected regions of the input character image according to the graphic attributes to find out the independent strokes. Finally, the instance segmentation-based stroke extraction method is applied to extract each individual stroke. Experimental results show that the average accuracy of the proposed method on the five test subsets of CSSCD, LTH, SS, HLJ, FZJTJW and FZLBJW is 94. 3\%, 96. 2\%, 91. 7\%, 92. 9\% and 89. 4\%, respectively, which is greatly improved compared with the existing methods, which proves the effectiveness of the method described in this paper. Our code and datasets are publicly available at https: //github. com/JacksonMa618/MLStroke