基于条件生成对抗网络的书法字笔画分割

张巍; 张筱; 万永菁

doi:10.16383/j.aas.c190141

基于条件生成对抗网络的书法字笔画分割

doi: 10.16383/j.aas.c190141

张巍^1,,
张筱^1,,
万永菁^1,

1.
华东理工大学信息科学与工程学院上海 200237

基金项目: 国家自然科学基金(61872143)资助

详细信息

作者简介:
张巍：华东理工大学信息科学与技术学院硕士研究生. 主要研究方向为数字图像处理. E-mail: johnwayne1995@163.com

张筱：华东理工大学信息科学与技术学院硕士研究生. 主要研究方向为模式识别. E-mail: zhangxiaoecust17@163.com

万永菁：华东理工大学信息科学与技术学院教授. 2008年获得华东理工大学检测技术与自动化装置专业博士学位. 主要研究方向为智能信息处理. 本文通信作者. E-mail: wanyongjing@ecust.edu.cn

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出版历程
- 收稿日期: 2019-03-07
- 录用日期: 2019-06-02
- 网络出版日期: 2022-06-14
- 刊出日期: 2022-07-01

Stroke Segmentation of Calligraphy Based on Conditional Generative Adversarial Network

1.
College of Information Sciences and Technology, East China University of Science and Technology, Shanghai 200237

Funds: Supported by National Natural Science Foundation of China (61872143)

More Information

Author Bio:
ZHANG Wei　Master student at the College of Information Sciences and Technology, East China University of Science and Technology. His main research interest is digital image processing

ZHANG Xiao　Master student at the College of Information Sciences and Technology, East China University of Science and Technology. Her main research interest is pattern recognition

WAN Yong-Jing　Professor at the College of Information Sciences and Technology, East China University of Science and Technology. She received her Ph.D. degree in detection technology and automatic equipment from East China University of Science and Technology in 2008. Her main research interest is intelligent information processing. Corresponding author of this paper

摘要

摘要: 毛笔书法作为中华传统艺术的精华, 需要在新的时代背景下继续传承和发扬. 书法字是以笔画为基本单元组成的复杂图形, 如果要分析书法结构, 笔画分割是首要的步骤. 传统的笔画分割方法主要利用细化法从汉字骨架上提取特征点, 分析交叉区域的子笔画拓扑结构关系来分割笔画. 本文分析了传统笔画分割基于底层特征拆分笔画的局限性, 利用条件生成对抗网络(Conditional generative adversarial network, CGAN)的对抗学习机制直接分割笔画, 使提取笔画从先细化再分割改进为直接分割. 该方法能有效提取出精确的笔画, 得到的高层语义特征和保留完整信息的单个笔画利于后续对书法轮廓和结构的评价.
- 书法结构 /
- 笔画分割 /
- 条件生成对抗网络 /
- 对抗学习
Abstract: As the essence of Chinese traditional art, brush calligraphy needs to continue to inherit and carry forward in the new era. Calligraphy is a complex figure composed of strokes as the basic unit. If you want to analyze the structure of calligraphy, stroke segmentation is the first step. The traditional stroke segmentation method mainly uses the refinement method to extract feature points from the Chinese character skeleton, and analyzes the sub-stroke topology relationship of the intersection region to segment the strokes. This paper analyzes the limitations of traditional stroke segmentation based on the underlying feature splitting strokes, and the strokes are directly segmented by using the adversarial learning mechanism of conditional generative adversarial network (CGAN). Improve the method of extracting strokes from first refinement and then segmentation to direct segmentation. This method can effectively extract accurate strokes. The resulting high-level semantic features and individual strokes that retain complete information are helpful for the subsequent evaluation of the outline and structure of calligraphy.
- Structure of calligraphy /
- stroke segmentation /
- conditional generative adversarial network (CGAN) /
- adversarial learning

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图 1 CGAN基本框架

Fig. 1 Basic framework of CGAN

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图 2 生成器网络结构

Fig. 2 Network structure of generator

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图 3 判别器网络结构

Fig. 3 Network structure of discriminator

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图 4 生成器训练过程

Fig. 4 Generator training process

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图 5 判别器训练过程

Fig. 5 Discriminator training process

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图 6 测试图像

Fig. 6 Test image

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图 7 模型训练不同代数的结果

Fig. 7 Model training results of different epoch

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图 8 损失函数在训练过程中的变化

Fig. 8 Change of loss function during training

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图 9 5张典型测试图像分割结果

Fig. 9 Five typical test image segmentation results

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图 10 传统算法骨架法流程

Fig. 10 Traditional algorithm skeleton method flow

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图 11 本文算法流程

Fig. 11 The algorithm flow

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图 12 传统算法(上)与本文算法(下)骨架对比

Fig. 12 Traditional algorithm (top) and the algorithm of this paper (bottom) extract skeleton comparison

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图 13 保留高层语义的两个笔画

Fig. 13 Two strokes of high-level semantics

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图 14 细化后的两个笔画

Fig. 14 Two strokes after thining

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表 1 笔画分割的性能

Table 1 Performance of stroke segmentation

笔画	1	2	3	4	5	6	7	8	9	10	11	12	13
AC	0.9996	0.9976	0.9988	0.9994	0.9996	0.9996	0.9986	0.9991	0.9991	0.9967	0.9992	0.9986	0.9983
F1	0.9592	0.9435	0.9604	0.9397	0.9710	0.9663	0.9519	0.9312	0.9610	0.9583	0.9483	0.9307	0.9572

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