基于联合两种特征的手写体维文字符识别  

Recognition of handwritten Uyghur character based on combination of two features

作　　者：姜文[1] 刘立康[1] JIANG Wen;LIU Likang(School of Telecommunication Engineering, Xidian University, Xi’an 710071, China)

机构地区：[1]西安电子科技大学通信工程学院,西安710071

出　　处：《计算机工程与应用》2017年第5期192-196,共5页Computer Engineering and Applications

基　　金：国家部委基础科研计划资助项目(No.D1120060967)

摘　　要：提出一种联合两种特征的手写体维文字符识别算法。该算法对手写体维文字符图像进行实值Gabor能量特征和方向线素网格特征的提取,将实值Gabor滤波器的128维能量特征和方向线素的128维网格特征结合起来,使用KNN分类器对两种特征进行联合分类。对手写体维文字符数据库中的样本分别进行手写体维文字符特征识别和维文字符笔迹特征识别。实验结果表明,和采用一种特征的识别算法比较,进一步提高了手写体维文字符的识别率。该算法也可用于手写体阿拉伯文字符的识别。This paper gives a recognition algorithm which combines 2 features of the hand-written Uyghur character. The algorithm extracts the real value Gabor energy feature and directional element feature of hand-written Uyghur character,combining the 128 dimension real value Gabor energy feature and the 128 dimension directional element feature together,uses KNN classifier to unitedly classify these two features. It recognizes the character feature and the handwriting feature for the samples in the database of the hand-written Uyghur character. The experimental result shows, the algorithm is more remarkable in recognition of the handwritten Uyghur character than using one feature. The algorithm can also be used in recognition of handwritten Arabic character.

关 键 词：手写体维文字符 GABOR滤波器 方向线素 K最近邻(KNN)识别分类器 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]