[1]张宁,秦德鑫,王秀芳.基于Matlab和C#的数显仪表数字识别系统[J].计算机技术与发展,2013,(09):70-73.
　ZHANG Ning[],QIN De-xin[],WANG Xiu-fang[].Digital Recognition System of Numerical Instruments Based on Matlab and C#[J].,2013,(09):70-73.
点击复制

基于Matlab和C#的数显仪表数字识别系统()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2013年09期
页码:
70-73
栏目:
智能、算法、系统工程
出版日期:
1900-01-01

文章信息/Info

Title:
Digital Recognition System of Numerical Instruments Based on Matlab and C#
文章编号:
1673-629X（2013）09-0070-04
作者:
张宁1; 秦德鑫2; 王秀芳2
[1]北京理工大学 信息与电子学院;[2]东北石油大学 电气信息工程学院
Author(s):
ZHANG Ning[1]; QIN De-xin[2]; WANG Xiu-fang[2]
关键词:
数字识别; 神经网络; 特征提取; 数显仪表
Keywords:
digital recognition; neural network; feature extraction; numeral instruments
文献标志码:
A
摘要:
数显仪表中数字的识别技术在仪表自动识别领域中应用广泛，改善其识别准确率有助于提高仪表自动化水平。文中采用Otsu算法实现图像二值化，基于孤立像素连通域法对二值图像去噪，并利用垂直投影算法完成字符的分割，利用模块法进行特征提取。构建了3层BP神经网络，采用自适应带动量项的方法对BP神经网络进行参数调整。基于动态链接库方法，文中设计了结合Matlab和C#的数字识别系统。测试结果表明，单字符图像识别准确率可达98%，多字符图像识别准确率可达92.5%
Abstract:
Digital recognition technology is widely applied in numerical instruments. Improving its recognition rate is helpful for increasing the automation level of instruments. In this paper,Otsu algorithm is used to realize the image binarization,isolated pixel connected domain is adopted to eliminate noise,vertical projection algorithm is applied to accomplish the character segmentation and modular algorithm is exploited to extract the feature. Furthermore,3-layered BP neural network is established,and the adaptive learning factor with a momen-tum term is used to adjust the parameters of BP neural network. Based on dynamic link library method,the composite Matlab and C# dig-ital recognition system is designed. The results indicate that the recognition rate can reach 98% for single-number images and 92. 5% for multi-number images

相似文献/References:

[1]路川 胡欣杰.区域航空市场航线客流量预测研究[J].计算机技术与发展,2010,(04):84.
　LU Chuan,HU Xin-jie.Analysis of Regional Airline Passenger Forecast Title[J].,2010,(09):84.
[2]高峥 陈蜀宇 李国勇.混合入侵检测系统的研究[J].计算机技术与发展,2010,(06):148.
　GAO Zheng,CHEN Shu-yu,LI Guo-yong.Research of a Hybrid Intrusion Detection System[J].,2010,(09):148.
[3]包力伟 周俊.铸锻企业生产质量控制系统的开发[J].计算机技术与发展,2008,(04):174.
　BAO Li-wei,ZHOU Jun.Development of a Manufacture Quality Control System in Casting Company[J].,2008,(09):174.
[4]李志俊 程家兴 金奎 饶玉佳.基于样本期望训练数的BP神经网络改进研究[J].计算机技术与发展,2009,(05):103.
　LI Zhi-jun,CHENG Jia-xing,JIN Kui,et al.BP Algorithm Improvement Based on Sample Expected Training Number[J].,2009,(09):103.
[5]李龙澍 葛瑞峰 王慧萍.基于神经网络的批强化学习在Robocup中的应用[J].计算机技术与发展,2009,(07):98.
　LI Long-shu,GE Rui-feng,WANG Hui-ping.Application of Batch Reinforcement Learning Based on NN to Robocup[J].,2009,(09):98.
[6]贾志先.神经网络在空白试卷识别中的应用[J].计算机技术与发展,2009,(08):208.
　JIA Zhi-xian.Application of Neural Network in Recognization Blank Examination Paper[J].,2009,(09):208.
[7]肖宜龙 路游 亓永刚.基于神经网络的NURBS曲面重建[J].计算机技术与发展,2009,(09):65.
　XIAO Yi-long,LU You,QI Yong-gang.NURBS Surface Reconstruction Based on Neural Network[J].,2009,(09):65.
[8]蔡秋茹 罗烨 柳益君 叶飞跃.企业资信的BP神经网络评估模型研究[J].计算机技术与发展,2009,(10):117.
　CAI Qiu-ru,LUO Ye,LIU Yi-jun,et al.Research on BP Neural Network Model for Corporation Credit Rating[J].,2009,(09):117.
[9]王晓敏 刘希玉 戴芬.BP神经网络预测算法的改进及应用[J].计算机技术与发展,2009,(11):64.
　WANG Xiao-min,LIU Xi-yu,DAI Fen.Improvement and Application of BP Neural Network Forecasting Algorithm[J].,2009,(09):64.
[10]崔海青 刘希玉.基于粒子群算法的RBF网络参数优化算法[J].计算机技术与发展,2009,(12):117.
　CUI Hai-qing,LIU Xi-yu.Parameter Optimization Algorithm of RBF Neural Network Based on PSO Algorithm[J].,2009,(09):117.

更新日期/Last Update: 1900-01-01