Coarse classification scheme for Chinese character based on RBF neural network

doi:10.3778/j.issn.1002-8331.2009.06.048

Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (6): 170-172.DOI: 10.3778/j.issn.1002-8331.2009.06.048

• 数据库、信号与信息处理 • Previous Articles Next Articles

Coarse classification scheme for Chinese character based on RBF neural network

YANG Fang,LI Hong-rui,TIAN Xue-dong

College of Mathematics and Computer Science，Hebei University，Baoding，Hebei 071002，China

Received:2008-01-16 Revised:2008-04-14 Online:2009-02-21 Published:2009-02-21
Contact: YANG Fang

基于RBF神经网络的汉字粗分类方法

杨芳,李红睿,田学东

河北大学数学与计算机学院，河北保定 071002

通讯作者: 杨芳

Abstract

Abstract: Coarse classification is the key to improve recognition speed.An improved coarse classification scheme based on RBF（Radial Basis Function） neural network for Chinese character is presented in this paper.Four-side code feature and gross meshed feature are respectly applied as coarse feature to compare in this experiment.The GB2312-80 first-level Chinese character samples including printed and handwritten form are objects in this experiment.Experiment results show that proposed method has excellent performance on coarse classification in contrast to Euclidean distance as classifier used in conventional method.

Key words: Radial Basis Function（RBF） neural network, coarse classification, four-side code, gross meshed feature

摘要： 粗分类是提高汉字识别速度的主要手段。将RBF（Radial Basis Function neural network）神经网络用于汉字粗分类，采用汉字四边码和粗网格作为汉字粗分类的特征以进行比较。分别对GB2312-80一级字库印刷体及手写体进行实验，实验结果表明将RBF神经网络用于汉字粗分类比通常使用的欧式距离作为分类器有较好的性能。

关键词: RBF神经网络, 粗分类, 四边码, 粗网格

YANG Fang,LI Hong-rui,TIAN Xue-dong. Coarse classification scheme for Chinese character based on RBF neural network[J]. Computer Engineering and Applications, 2009, 45(6): 170-172.

杨芳,李红睿,田学东. 基于RBF神经网络的汉字粗分类方法[J]. 计算机工程与应用, 2009, 45(6): 170-172.

[1]	ZHOU Youhang, ZHAO Hanyun, LIU Hanjiang, LI Yuze, XIAO Yuqin. Self-Learning Gait Planning Method for Biped Robot Using DDPG [J]. Computer Engineering and Applications, 2021, 57(6): 254-259.
[2]	SUN Tangle, LI Guohui. EEMD and RBF neural network prediction of sunspot monthly mean [J]. Computer Engineering and Applications, 2017, 53(24): 252-256.
[3]	LU Yanan, NAN Jingchang, GAO Mingming. RBF neural network for modeling based on improved parallel particle swarm optimization [J]. Computer Engineering and Applications, 2017, 53(14): 45-50.
[4]	TAN Qian1, JIANG Yi1, LIN Fan2. Load prediction of Swift cloud storage based on AHP-RBF [J]. Computer Engineering and Applications, 2014, 50(8): 35-39.
[5]	CHEN Hongxing. Network intrusion detection based on neural network optimized by GA [J]. Computer Engineering and Applications, 2014, 50(14): 78-81.
[6]	GUO Xiaoyan1, ZHANG Ming2. Method of personal credit evaluation of bank based on RBF neural network with weight [J]. Computer Engineering and Applications, 2013, 49(5): 258-262.
[7]	QIU Jiandong, JIANG Zhaoyuan. Research of railway freight volume prediction based on NLA-PSO-RBF [J]. Computer Engineering and Applications, 2013, 49(22): 253-257.
[8]	LI Guosheng1, WANG Lianhong1, DAI Yuxing1, WANG Xingxian2. Application of motor based on PSO and RBF neural network [J]. Computer Engineering and Applications, 2012, 48(31): 216-219.
[9]	PANG Zhen, XU Weihong. Learning algorithm for RBF neural networks based on improved k-means algorithm [J]. Computer Engineering and Applications, 2012, 48(11): 161-163.
[10]	LI Hui, GU Shenming. Approach to red tide prediction on RBF neural network [J]. Computer Engineering and Applications, 2012, 48(1): 228-230.
[11]	ZHOU Baomin1，LIAO Ying2. Adaptive improved RBF neural network sliding mode control for unknown nonlinear systems [J]. Computer Engineering and Applications, 2011, 47(9): 243-245.
[12]	NAN Jingchang1，ZHAO Jingmei1，YUAN Jie2. Research on behavioral models for RF power amplifier based on RBF neural network [J]. Computer Engineering and Applications, 2011, 47(8): 125-127.
[13]	TAN Chang，XIAO Nanfeng. Static hand gesture recognition based on improved RCE neural network and RBF neural network [J]. Computer Engineering and Applications, 2011, 47(7): 172-176.
[14]	WANG Lili，QIAO Liyong，CHEN Yu，CHEN Deyun. Flow pattern identification based on feature extraction and RBF neural network for Electrical Capacitance Tomography [J]. Computer Engineering and Applications, 2011, 47(5): 175-178.
[15]	WEN Xinxiu1，XU Guangning2. Research on DNA immune genetic algorithm and its application [J]. Computer Engineering and Applications, 2011, 47(26): 55-57.

Coarse classification scheme for Chinese character based on RBF neural network

基于RBF神经网络的汉字粗分类方法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics