Stroke Extraction of Chinese Character Based on Deep Structure Deformable Image Registration
Keywords:CV: Other Foundations of Computer Vision, CV: Applications, CV: Biometrics, Face, Gesture & Pose, CV: Computational Photography, Image & Video Synthesis, CV: Medical and Biological Imaging, CV: Object Detection & Categorization
AbstractStroke 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.
How to Cite
Li, M., Yu, Y., Yang, Y., Ren, G., & Wang, J. (2023). Stroke Extraction of Chinese Character Based on Deep Structure Deformable Image Registration. Proceedings of the AAAI Conference on Artificial Intelligence, 37(1), 1360-1367. https://doi.org/10.1609/aaai.v37i1.25220
AAAI Technical Track on Computer Vision I