gusucode.com > 字母识别项目matlab源码程序 > shibie.m

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%%%%%%%识别26个大写字母%%%%%%%%
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clc;
clear all;
disp('回车键 ...')
chos=input('请直接按回车键正在生成输入向量和目标向量，请稍等… ');
if isempty(chos), chos=0; end 
if chos==0,
for kk=0:256 
    p1=ones(16,16);% 初始化16×16的二值图像像素值(全白)   
    m =strcat('nums\',int2str(kk),'.bmp');% 形成训练样本图像的文件名(0～89.bmp)    
    x=imread(m,'bmp');% 读入训练样本图像文件   
    bw=im2bw(x,0.5);% 将读入的训练样本图像转换为二值图像  
    [i,j]= find(bw==0);% 寻找二值图像中像素值为0(黑)的行号和列号  
    imin=min(i);% 寻找二值图像中像素值为0(黑)的最小行号 
    imax=max(i);% 寻找二值图像中像素值为0(黑)的最大行号   
    jmin=min(j);% 寻找二值图像中像素值为0(黑)的最小列号   
    jmax=max(j);% 寻找二值图像中像素值为0(黑)的最大列号   
    bw1=bw(imin:imax,jmin:jmax);% 截取图像像素值为0(黑)的最大矩形区域 
    rate=16/max(size(bw1));% 计算截取图像转换为16×16的二值图像的缩放比例
    bw1=imresize(bw1,rate);% 将截取图像转换为16×16的二值图像(由于缩放比例  
                               % 大多数情况下不为16的倍数,所以可能存在转换误差) 
   [i,j]=size(bw1);% 转换图像的大小   
   i1=round((16-i)/2);% 计算转换图像与标准16×16的图像的左边界差    
   j1=round((16-j)/2);% 计算转换图像与标准16×16的图像的上边界差    
   p1(i1+1:i1+i,j1+1:j1+j)=bw1;% 将截取图像转换为标准的16×16的图像 
   p1= -1.*p1+ones(16,16);% 反色处理    % 以图像数据形成神经网络输入向量 
   for m=0:15       
   p(m*16+1:(m +1)*16,kk+1)=p1(1:16,m+1);   
end    % 形成神经网络目标向量  
switch kk    
case{0,1,2,3,4,5,6,7,8,9}  % 字母A       
    t(kk+1)=0;   
case{10,11,12,13,14,15,16,17,18,19}  % 字母B    
    t(kk+1)=1;    
case{20,21,22,23,24,25,26,27,28,29}  % 字母C      
    t(kk+1)=2;    
case{30,31,32,33,34,35,36,37,38,39}  % 字母D      
    t(kk+1)=3;    
case{40,41,42,43,44,45,46,47,48,49}  %字母E           
    t(kk+1)=4;       
case{50,51,52,53,54,55,56,57,58,59}  %字母F        
    t(kk+1)=5;    
case{60,61,62,63,64,65,66,67,68,69}  % 字母G       
    t(kk+1)=6;     
case{70,71,72,73,74,75,76,77,78,79}  % 字母H        
    t(kk+1)=7;            
case{80,81,82,83,84,85,86,87,88,89}  % 字母I     
    t(kk+1)=8;       
case{90,91,92,93,94,95,96,97,98,99}  % 字母J      
    t(kk+1)=9; 
case{100,101,102,103,104,105,106,107,108,109}  % 字母K     
    t(kk+1)=10;
case{110,111,112,113,114,115,116,117,118,119}  % 字母L     
    t(kk+1)=11; 
case{120,121,122,123,124,125,126,127,128,129}  % 字母M    
    t(kk+1)=12; 
case{130,131,132,133,134,135,136,137,138,139}  % 字母N     
    t(kk+1)=13;
case{140,141,142,143,144,145,146,147,148,149}  % 字母O      
    t(kk+1)=14;
case{150,151,152,153,154,155,156,157,158,159}  % 字母P      
    t(kk+1)=15;
case{160,161,162,163,164,165,166,167,168,169}  % 字母Q      
    t(kk+1)=16; 
case{170,171,172,173,174,175,176,177,178,179}  % 字母R     
    t(kk+1)=17; 
case{180,181,182,183,184,185,186,187,188,189}  % 字母S     
    t(kk+1)=18; 
case{190,191,192,193,194,195,196,197,198,199}  %字母T     
    t(kk+1)=19; 
case{200,201,202,203,204,205,206,207,208,209}  % 字母U     
    t(kk+1)=20;
case{210,211,212,213,214,215,216,217,218,219}  % 字母V      
    t(kk+1)=21; 
case{220,221,222,223,224,225,226,227,228,229}  % 字母W      
    t(kk+1)=22;
case{230,231,232,233,234,235,236,237,238,239}  % 字母X    
    t(kk+1)=23; 
case{240,241,242,243,244,245,246,247,248,249}  % 字母Y     
    t(kk+1)=24; 
case{250,251,252,253,254,255,256,257,258,259}  % 字母Z      
    t(kk+1)=25;    
end
end
end
save E52PT p t;    % 存储形成的训练样本集(输入向量和目标向量)
disp('输入向量和目标向量生成结束！')
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%神经网络的训练%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear all;
disp('     ');
chos=input('请按回车键进行神经网络的训练 ');
if isempty(chos), chos=0; end 
if chos==0,
load E52PT p t;    % 加载训练样本集（输入向量和目标向量）
% 创建BP网
pr(1:256,1)=0;
pr(1:256,2)=1;
net= newff(pr,[26 1],{'logsig','purelin'},'traingdx','learngdm');
% 设置训练参数和训练BP网络
net.trainParam.epochs = 5000;
net.trainParam.goal= 0.002;
net.trainParam.show = 100;%步长为100
net.trainParam.lr=0.05;
net= train(net,p,t);
end
% 存储训练后的BP网络
save E52net net;