CAJ | 학술논문

针对手绘草图识别算法大多采用限制用户绘制习惯来实现笔画分组的问题,提出一种基于贝叶斯网络的手绘草图识别算法.该算法将手绘草图识别中的笔西分组和符号识别统一为一个过程,用贝叶斯网络拓扑结构来表达草图结构信息.基于该网络,根据最大后验概率对连续输入的笔画进行动态最优分组,同时在线预测每组笔画的符号类别.实验结果表明,该方法是一种有效的在线递进式笔画分组和识别算法,在电路符号手绘识别中达到71.3%的过程识别率和85%的最终识别率.
침대수회초도식별산법대다채용한제용호회제습관래실현필화분조적문제,제출일충기우패협사망락적수회초도식별산법.해산법장수회초도식별중적필서분조화부호식별통일위일개과정,용패협사망락탁복결구래표체초도결구신식.기우해망락,근거최대후험개솔대련속수입적필화진행동태최우분조,동시재선예측매조필화적부호유별.실험결과표명,해방법시일충유효적재선체진식필화분조화식별산법,재전로부호수회식별중체도71.3%적과정식별솔화85%적최종식별솔.
To solve the limitation of restricting the user's drawing style during the sketch grouping and recognition,a Bayesian network based sketch recognition algorithm is proposed.The algorithm combines the sketch grouping and the graphic symbol recognition into a unified procedure,which represents the sketch structure information as a Bayesian network.Based on the network,the growing sketches are grouped according to the maximum posterior probability,and each sketch group is recognized as a predefined symbol simultaneously.Experimental results show the effectiveness for the progressive sketch grouping and recognition.which has 71.3%procedure recognition rate and 85%final recognition rate.