数字识别m文件（Digital identification m file）.docVIP

数字识别m文件（Digital identification m file） File header: %----------------------------------------------------------------- %Digit_Recognition.m Developed by Rentian Huang, %Hope University, Distribute System Emailhope.ac.uk %----------------------------------------------------------------- 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 The end of the file: Late 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 b0 B=b+1; Elseif b9; 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 %-------------------------------------------------------------------------- The other one File header: %qq:445282655 The selected sample: usps_train% % usage: BP neural network classification method % detection rate: 64.55% CLC Clear Close all % in ascil format to read the training set Load (-ascii,train.txt); InNum=256; HideNum=512; OutNum=4; Rate_w=0.3;% weight le