基于多标签语义分割的硬笔字笔画提取  

Stroke Extraction for Chinese Handwriting Character Based on Multi-label Semantic Segmentation

作　　者：余嘉云[1] 李丁宇 徐占洋 王晶弘 林巍 YU Jia-Yun;LI Ding-Yu;XU Zhan-Yang;WANG Jing-Hong;LIN Wei(Nanjing Normal University,Nanjing 210023,China;Nanjing University of Information Science&Technology,Nanjing 210044,China;Nanjing Technology R&D Center,Jiangsu Children’s Spring Interconnection Education Technology Co.Ltd.,Nanjing 210032,China)

机构地区：[1]南京师范大学,南京210023 [2]南京信息工程大学,南京210044 [3]江苏少儿春互联教育科技有限公司南京技术研发中心,南京210032

出　　处：《计算机系统应用》2024年第9期174-182,共9页Computer Systems & Applications

摘　　要：汉字作为中华文化的载体,因其复杂的结构区别于其他文字.笔画作为汉字的基本单元,在硬笔字评价中起到至关重要的作用.正确提取笔画,是硬笔字评价的首要步骤.现有的笔画提取方法多数是基于规则的,由于汉字的复杂性,这些规则通常无法顾及所有特征,且在评价时无法根据笔顺等信息与模板字笔画匹配.为了解决这些问题,该文将笔画提取转化为多标签语义分割问题,提出了多标签语义分割模型(M-TransUNet),利用深度卷积模型以汉字为单位任务进行训练,保留了笔画原有结构,避免了笔画段组合的二义性,同时得到了硬笔字的笔顺,有利于笔画评价等下游任务.由于硬笔字图像只分为前景和背景,没有额外颜色信息,所以更容易产生FP(false positive)分割噪声.为解决此问题,本文还提出了一种针对笔画分割结果的局部平滑策略(local smooth strategy on stroke,LSSS),淡化噪声的影响.最后,本文对M-TransUNet的分割性能以及效率进行了实验,证明了本文算法在很小性能损失的情况下,极大地提升了效率.同时对LSSS算法进行了实验,证明其在FP噪声消除的有效性.As the carrier of Chinese culture,Chinese characters are distinguished from other scripts by their complex structure.As the basic unit of Chinese characters,strokes play a vital role in the evaluation of Chinese handwriting characters.The correct extraction of strokes is the primary step in evaluating Chinese handwriting characters.Most existing stroke extraction methods are based on specific rules,and due to the complexity of Chinese characters,these rules usually cannot take into account all the features,and cannot match the strokes of template characters based on stroke order and other information during evaluation.To address these issues,this study transforms stroke extraction into a multi-label semantic segmentation problem and proposes a multi-label semantic segmentation model(M-TransUNet),which utilizes a deep convolutional model to train with Chinese characters as a unit task,retaining the original structure of the strokes and avoiding ambiguity in stroke segment combinations.At the same time,the stroke order of the Chinese handwriting characters is obtained,which is conducive to downstream tasks,such as stroke evaluations.Since the handwriting images are only divided into foreground and background without additional color information,they are more prone to generating FP segmentation noise.To solve this problem,this study also proposes a local smooth strategy on strokes(LSSS)for the stroke segmentation results to dilute the impact of noise.Finally,this study conducted experiments on the segmentation performance and efficiency of M-TransUNet,demonstrating that the algorithm significantly enhances efficiency with minimal performance loss.Additionally,experiments were carried out on the LSSS algorithm to demonstrate its effectiveness in eliminating FP noise.

关 键 词：硬笔字 笔画提取 多标签语义分割 局部平滑策略 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] J292.12[自动化与计算机技术—计算机科学与技术]