Stroke extraction of Chinese characters plays an important role in the field of character recognition and generation. The most existing character stroke extraction methods focus on image morphological features. These methods usually lead to errors of cross strokes extraction and stroke matching due to rarely using stroke semantics and prior information. In this paper, we propose a deep learning-based character stroke extraction method that takes semantic features and prior information of strokes into consideration. This method consists of three parts: image registration-based stroke registration that establishes the rough registration of the reference strokes and the target as prior information; image semantic segmentation-based stroke segmentation that preliminarily separates target strokes into seven categories; and high-precision extraction of single strokes. In the stroke registration, we propose a structure deformable image registration network to achieve structure-deformable transformation while maintaining the stable morphology of single strokes for character images with complex structures. In order to verify the effectiveness of the method, we construct two datasets respectively for calligraphy characters and regular handwriting characters. The experimental results show that our method strongly outperforms the baselines. Code is available at https://github.com/MengLi-l1/StrokeExtraction.

翻译：汉字笔画提取在字符识别与生成领域具有重要作用。现有的大多数笔画提取方法主要依赖图像形态特征，由于较少利用笔画语义与先验信息，常导致交叉笔画提取及笔画匹配错误。本文提出一种基于深度学习的汉字笔画提取方法，融合了笔画的语义特征与先验信息。该方法包含三个部分：基于图像配准的笔画配准，建立参考笔画与目标笔画间的粗略配准作为先验信息；基于图像语义分割的笔画分割，将目标笔画初步划分为七类；以及单笔画高精度提取。在笔画配准中，我们提出一种结构可变形图像配准网络，在保持复杂结构字符图像中单笔画形态稳定的同时，实现结构可变形变换。为验证方法有效性，我们分别构建了书法字与规则手写字两个数据集。实验结果表明，本方法显著优于基线模型。代码可见于https://github.com/MengLi-l1/StrokeExtraction。