trainchar.m

利用BP神经网络进行字符识别的系统源码

% nn character trainning
clear all;
for kk=0:49
    p1=ones(16,16);   %all white
    m=strcat('e:\doctordip\imgtemp\photo\',int2str(kk),'.bmp');
    x=imread(m);
    bw=im2bw(x,0.5);
    [i,j]=find(bw==0);   %find black
    imin=min(i);
    imax=max(i);
    jmin=min(j);
    jmax=max(j);
    bw1=bw(imin:imax,jmin:jmax);
    rate=16/max(size(bw1));
    bw1=imresize(bw1,rate);
    
    [i,j]=size(bw1);
    i1=round((16-i)/2);
    j1=round((16-j)/2);
    p1(i1+1:i1+i,j1+1:j1+j)=bw1;
    p1=-1.*p1+ones(16,16);
    
    for m=0:15
        ph(m*16+1:(m+1)*16,kk+1)=p1(m+1,1:16).';%row character
        pv(m*16+1:(m+1)*16,kk+1)=p1(1:16,m+1); %column character
        %pd(m*16+1:(m+1)*16,kk+1)=p1(m+1:m+1);
    end
    
    %add noise
    [Rh,Qh] = size(ph);
    Ph = ph + randn(Rh,Qh)*0.2;
    [Rv,Qv] = size(pv);
    Pv = pv + randn(Rv,Qv)*0.2;
    
    switch kk
        case{0,10,20,30,40}
            t(kk+1)=0;
        case{1,11,21,31,41}
            t(kk+1)=1;
        case{2,12,22,32,42}
            t(kk+1)=2;
        case{3,13,23,33,43}
            t(kk+1)=3;
        case{4,14,24,34,44}
            t(kk+1)=4;
        case{5,15,25,35,45}
            t(kk+1)=5;
        case{6,16,26,36,46}
            t(kk+1)=6;
        case{7,17,27,37,47}
            t(kk+1)=7;
        case{8,18,28,38,48}
            t(kk+1)=8;
        case{9,19,29,39,49}
            t(kk+1)=9;
    end
end

save EPTh  ph t;
save EPTv  pv t;
%save EPTd  pd t;