CAJ | 학술논문

针对现有多种分类器对具有不确定字形的古汉字识别精度不高的问题，提出了一种基于混合核加权最小二乘支持向量回归（WLS‐SVR）的古汉字识别算法．WLS‐SVR 的权重系数采用预测误差的指数衰减函数，混合核是由具有良好局域特性的小波核函数与具有良好全局特性的 RBF 核函数构成．在特征提取阶段，由于全局点密度与部件结构具有全局特征，而伪二维弹性网格与局部点密度具有局部特征，因此融合了古汉字的全局和局部特征．仿真实验表明，该算法具有较高的准确率与良好的鲁棒性．
침대현유다충분류기대구유불학정자형적고한자식별정도불고적문제，제출료일충기우혼합핵가권최소이승지지향량회귀（WLS‐SVR）적고한자식별산법．WLS‐SVR 적권중계수채용예측오차적지수쇠감함수，혼합핵시유구유량호국역특성적소파핵함수여구유량호전국특성적 RBF 핵함수구성．재특정제취계단，유우전국점밀도여부건결구구유전국특정，이위이유탄성망격여국부점밀도구유국부특정，인차융합료고한자적전국화국부특정．방진실험표명，해산법구유교고적준학솔여량호적로봉성．
The shapes of ancient Chinese characters are often uncertain , which reduces the accuracy of recognition by many classifiers .To solve this problem ,a new recognition algorithm combining adaptive weighted least squares support vector regression (WLS‐SVR ) with hybrid kernel function was proposed to recognize ancient Chinese characters .The weight coefficients of WLS‐SVR decayed at a rate of the exponential function of prediction errors .The hybrid kernel was constructed using the wavelet kernel function with local properties and RBF kernel function with global properties .For feature extraction ,global point density and component structure are fused with local features of pseudo 2D elastic mesh and local point density .Experiment results show the good robustness and high recognition accuracy of the proposed method .