笔画节点在手写体汉字识别中的作用  

The role of stroke nodes in the recognition of handwritten Chinese characters

作　　者：朱一鸣 赵阳[1] 唐宁[1] 周吉帆[1] 沈模卫[1] ZHU Yiming;ZHAO Yang;TANG Ning;ZHOU Jifan;SHEN Mowei(Department of Psychology and Behavioural Sciences,Zhejiang University,Hangzhou 310058,China)

机构地区：[1]浙江大学心理与行为科学系,杭州310058

出　　处：《心理学报》2023年第12期1903-1916,共14页Acta Psychologica Sinica

基　　金：国家自然科学基金面上项目(32071044、31871096);中央高校基本科研业务费专项资金资助(2021FZZX001-06)。

摘　　要：产生式理论认为,视觉图形的识别是对其产生过程的逆推理。汉字是笔画按正字法规则交错连接构成的象形文字,手写体汉字识别可以认为是对汉字产生过程的反向推理。基于典型的产生式模型——贝叶斯规划学习模型,汉字的产生式识别过程从识别字的笔画开始,先基于线段交点提取出节点,再枚举能产生该节点的所有笔画组合方式,从而获得汉字的产生方式。据此预测,节点数量和节点复杂度是手写汉字识别过程的重要影响因素。本研究通过三个实验考察了节点在汉字识别中的作用。结果显示,含有较多节点的汉字具有更好的识别绩效(节点数量效应),掩盖由较多笔画构成的高复杂度节点会对汉字识别产生更大干扰(节点复杂度效应)。本研究增进了对汉字识别早期过程的认识,为字形识别的产生式反向推理过程提供了证据。Generative theory holds that the recognition of visual graphics is the inverse reasoning of its generation process.Chinese characters are hieroglyphs formed by interlacing strokes according to orthographic rules.Chinese character recognition can be regarded as the reverse reasoning of the generation process of Chinese characters.Based on the typical generative model--Bayesian program learning model,the recognition of Chinese characters starts from recognizing the strokes.Firstly,the nodes are extracted based on the intersection of lines,and then all the stroke combination modes that can generate the node are enumerated to obtain the generation mode of Chinese characters.According to the above prediction,the number of nodes and node complexity are important factors in the process of Chinese character recognition.This study investigated the role of nodes in Chinese character recognition through three experiments.If the nodes provide guidance information for stroke segmentation,the more nodes,the better the performance of Chinese character recognition.In Experiment 1,we tested whether characters with more nodes have recognition advantages by adopting a 2×2 within-subjects design and using 76 single characters as the materials.Characters were chosen from two groups(high node-count and low node-count)of true characters,and two groups(high node-count and low node-count)of fake characters.The characters were briefly presented(10 ms,20 ms,30 ms,40 ms,50 ms,60 ms)and appeared once at each presentation time.The presentation order of stimuli was completely random.Each participant completed a total of 456 trials.Twenty-six participants joined in the experiment.After observing each character,the participants reported whether it was a true character or a fake one.If high complex nodes in a larger stroke space provide more information,covering high complex nodes will cause greater interference to character recognition.In Experiment 2,we tested whether characters covered the high complex nodes are harder to recognize by adoptin

关 键 词：手写汉字识别 节点 笔画 产生式模型 

分 类 号：B842[哲学宗教—基础心理学]