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%Digit_Recognition.m Developed by Rentian Huang,
%Hope University,Distribute System Email:10076507@hope.ac.uk
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clear all;
p(1:256,1)=1;
load digit net;                               %Load the BP Neural Networks which been trained
test=input('Please input a digits image:','s'); %Prompt for input digit image for recognize
s=input('Please input the number of digits:'); %Prompt for input number of digits
x=imread(test,'bmp');                          %read the input image
xbw=im2bw(x,0.9);                              %Convert the image to Black and White image
xbw=medfilt2(xbw);%Use medium filter if needed
bw=xbw;
result='';

%The loops for image segmentation
for n=0:s-1
    p1=ones(16,16);                            %Create an 16*16 Black and whit image (all white)
    [c,l]=size(bw);                            %Get the size of input image
    [i,j] = find(bw==0);                       %Find out the rows and columns which value is 0
    jmin=min(j);                               %Find out the smallest column which value is 0
    xbwt=bw(:,jmin:l);                         %Cut the image on the left of the image column which value is 1
    [c,l]=size(xbwt);                          %Get the image size after cuting 
    [i,j]=find(all(xbwt(1:c,:)==1));           %Find out the rows and columns which value is 1
    jmin=min(j);                               %Find out the smallest column in the new image
    xbwn{n+1}=xbwt(:,1:jmin-1);                %Cut number n digit image
    bw=xbwt(:,jmin:l);                         %Save new image after cutting number n digit image
    [i,j] = find(xbwn{n+1}==0);                %Find out all the rows and columns in number n image
    imin=min(i);                               %Find out the smallest row which value is 0
    imax=max(i);                               %Find out the largest row which value is 0
    xbwn1{n+1}=xbwn{n+1}(imin:imax,:);         %Keep the number n image after cutting the 0 rows
    rate=16/max(size(xbwn1{n+1}));             %Calculate the rate between number n image and 16*16 image
    xbwn1{n+1}=imresize(xbwn1{n+1},rate);      %Convert the image to 16*16 size
    [i,j]=size(xbwn1{n+1});                    %The size of number n digit
    i1=round((16-i)/2);                        %Calculate the row size different between image and 16
    j1=round((16-j)/2);                        %Calculate the column size different between image and 16
    p1(i1+1:i1+i,j1+1:j1+j)=xbwn1{n+1};        %Convert the image to standard 16*16 image
    xp(1:16,n*16+1:(n+1)*16)=p1;               %Save the number n digit image
    p1=-1.*p1+ones(16,16);                     %Reverse the value 0 and 1 with each other in the image
    for m=0:15
        p(m*16+1:(m+1)*16,1)=p1(1:16,m+1);     %Form the vector of input digit image to the BP NN
    end
    [a,Pf,Af] = sim(net,p);                    %BP NN simulate
    b=round(a);%The output digit from the network
    if b<0
        b=b+1;
    elseif b>9;
        b=b-1;
    end 
    result=strcat(result,num2str(b));          %Display the output of the NN
end         
imshow(xp);                                    %Display the input image 
title('The input Numbers image:')
text(0,60,strcat('Recognition Result：',result),'FontSize',10);%Display the results
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