【手写数字识别】基于BP神经网络手写数字识别系统含Matlab源码-CFANZ编程社区

1 简介

介绍了光学字符识别的几种方法以及神经网络的特点,神经网络技术能够解决传统OCR方法所不能解决的问题,同时指出了手写数字识别存在的困难,论证了利用神经网络技术解决这种困难的可能性.本文实现了通过一个含有1个隐藏层的BP网络来识别手写数字,并取得了良好效果,论证了这种技术用于手写数字识别的可行性.

2 部分代码

function varargout = szsb(varargin)
% SZSB M-file for szsb.fig
%      SZSB, by itself, creates a new SZSB or raises the existing
%      singleton*.
%
%      H = SZSB returns the handle to a new SZSB or the handle to
%      the existing singleton*.
%
%      SZSB('CALLBACK',hObject,eventData,handles,...) calls the local
%      function named CALLBACK in SZSB.M with the given input arguments.
%
%      SZSB('Property','Value',...) creates a new SZSB or raises the
%      existing singleton*.  Starting from the left, property value pairs are
%      applied to the GUI before szsb_OpeningFunction gets called.  An
%      unrecognized property name or invalid value makes property application
%      stop.  All inputs are passed to szsb_OpeningFcn via varargin.
%
%      *See GUI Options on GUIDE's Tools menu.  Choose "GUI allows only one
%      instance to run (singleton)".
%
gui_Singleton = 1;
gui_State = struct('gui_Name',       mfilename, ...
                   'gui_Singleton',  gui_Singleton, ...
                   'gui_OpeningFcn', @szsb_OpeningFcn, ...
                   'gui_OutputFcn',  @szsb_OutputFcn, ...
                   'gui_LayoutFcn',  [] , ...
                   'gui_Callback',   []);
if nargin && ischar(varargin{1})
    gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
    gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
% --- Executes just before szsb is made visible.
function szsb_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject    handle to figure
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
% varargin   command line arguments to szsb (see VARARGIN)
% Choose default command line output for szsb
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes szsb wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = szsb_OutputFcn(hObject, eventdata, handles) 
% varargout  cell array for returning output args (see VARARGOUT);
% hObject    handle to figure
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
varargout{1} = handles.output;
% --- Executes when figure1 is resized.
function figure1_ResizeFcn(hObject, eventdata, handles)
% hObject    handle to figure1 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
% --------------------------------------------------------------------
function Untitled_2_Callback(hObject, eventdata, handles)
% hObject    handle to Untitled_2 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
axes(handles.axes1);
clear all;
global im
global p1
global p5
global p3
global p4
global bw
global bw1
global bw2
global n
global t1
global i3
global i4
global i2
global b
global gray
filename=0;
[filename,pathname]=...
        uigetfile({'*.bmp'},'请选择图片');
 str=[pathname filename];
 l=length(filename);
 if l==1
     return;
 end
 test=filename(1,l-2:l);
 if test=='bmp'
 else 
     msgbox('请选择bmp文件','错误');
     return;
 end
im=imread(str);
imshow(im);
if(length(size(im))==3)
       gray = rgb2gray(im); %图像灰度化
end
p1=zeros(16,16); 
bw=im2bw(im,0.9); %图像二值化
[l,r]=size(bw);
i4=bw;
    for i=1:l
        for j=1:r
            if i4(i,j)==0
                i4(i,j)=1;
            else
                i4(i,j)=0;
            end
        end
    end
i2 = bwmorph(i4,'majority','inf');%图像细化
i3=i2;
[i,j]=find(i3==1);
imin=min(i);
imax=max(i);
jmin=min(j);
jmax=max(j);
lab=false;%lab 用作是否进入一个字符分割的标志
k=1;
for j = jmin:jmax+1
    if (max(size(find(i3(imin:imax,j)==1)))-1)==0 %在第j列中没有找到像素为1（白点）的点
        if lab==true;
            t1(1,k)=j-1;  %t1的第一行偶数记录分割数字的右边界
            k=k+1;
            lab=false;
        end     
    else%在第j列中存在像素为1（白点）的点
        if lab==false
            lab=true;
            t1(1,k)=j; %t1的第一行奇数记录分割数字的左边界
            k=k+1;
        end
    end
end
n=max(size(t1))/2;%m为待识别数字的个数
for i=1:n   
    j=2*i;
    for k=imin:imax%由上到下寻找上边界
         if (max(size(find(i3(k,t1(1,j-1):t1(1,j))==1)))-1)>0% 在对应的列中找到了分割数字的上边界
             t1(2,j-1)=k;  %t1的第二行奇数列分别记录分割数字的上边界
             break;
         end
    end
end
for i=1:n   
    j=2*i;
    for k=imax:-1:imin%由下到上寻找下边界
         if (max(size(find(i3(k,t1(1,j-1):t1(1,j))==1)))-1)>0% 在对应的列中找到了分割数字的下边界
             t1(2,j)=k;  %t1的第二行偶数列分别记录分割数字的下边界
             break;
         end
    end
end
    p4=zeros(l,r);
    p5=zeros(l,r);
    load numbernet net;
for i=1:n
        j=2*i;
        bw1=i3(t1(2,j-1):t1(2,j),t1(1,j-1):t1(1,j)); 
        p1=zeros(16,16);
        rate=16/max(size(bw1));
        bw1=imresize(bw1,rate);
        [z,x]=size(bw1);
        i1=round((16-z)/2);
        j1=round((16-x)/2);
        p1(i1+1:i1+z,j1+1:j1+x)=bw1;        
        p4(t1(2,j)-15:t1(2,j),t1(1,j-1):t1(1,j-1)+15)=p1;      
        p1 = bwmorph(p1,'thin',inf);
        p5(t1(2,j)-15:t1(2,j),t1(1,j-1):t1(1,j-1)+15)=p1;       
             for m=0:15
                if(0<=m&&m<=3)
                    mm=(m+1)*4;
                    p(m+1,1)=length(find(p1(1:4,mm-3:mm)==1));