基于最小二乘支持向量机的压印字符识别方法

上海大学学报(自然科学版)

基于最小二乘支持向量机的压印字符识别方法

李国平1,2，路长厚1，李健美1

1.山东大学机械学院，济南 250061； 2.济南大学机械学院，济南 250022

收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2007-04-30 发布日期:2007-04-30
通讯作者: 路长厚

Pressed Protuberant Character Recognition Based on Least Squares Support Vector Machines

LI Guo-ping1,2, LU Chang-hou1, LI Jian-mei1

1. School of Mechanical Engineering, Shandong University, Jinan 250061, China; 2. School of Mechanical Engineering, University of Jinan, Jinan 250022, China

Received:1900-01-01 Revised:1900-01-01 Online:2007-04-30 Published:2007-04-30
Contact: LU Chang-hou

摘要/Abstract

摘要： 将最小二乘支持向量机引入到小字符集压印字符识别中.首先介绍最小二乘支持向量机的基本原理和主要算法，然后在实验中采用最小二乘支持向量机训练软件，针对标牌上的压印字符的数字集进行仿真，同时与神经网络等其他分类方法进行比较.实验结果表明此方法的识别率较高，在小字符集识别中具有较强的实用性.

关键词: 压印字符, 字符识别, 最小二乘支持向量机

Abstract: This paper presents an application of least squares support vector machines in small-set pressed protuberant character recognition. The theory and algorithms of least squares support vector machines are introduced. Least squares support vector machines are used to train the software in the experiment for simulation of labels' pressed protuberant characters, and compare with the results of neural network classification, et al. Experiment results show that the least squares support vector machines method has high recognition rate and is practical.

Key words: character recognition, pressed protuberant characters, least squares support vector machines

中图分类号:

TP 391.4

李国平;路长厚;李健美. 基于最小二乘支持向量机的压印字符识别方法[J]. 上海大学学报(自然科学版).

LI Guo-ping;LU Chang-hou;LI Jian-mei. Pressed Protuberant Character Recognition Based on Least Squares Support Vector Machines
[J]. Journal of Shanghai University（Natural Science Edition）.

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