基于SVM的手写字体识别.rar_手写 识别_手写体识别_手写字体_手写识别

function Chapter_CharacterRecognitionUsingLibsvm %% Matlab神经网络43个案例分析 % 基于SVM的手写字体识别 % by 李洋(faruto) % http://www.matlabsky.com % Email:[email protected] % http://weibo.com/faruto % http://blog.sina.com.cn/faruto % 2013.01.01 %% A Little Clean Work close all; clear; clc; format compact; %% 载入训练数据 % 利用uigetfile函数交互式选取训练样本 [FileName,PathName,FilterIndex] = uigetfile( ... {'*.jpg';'*.bmp'},'请导入训练图片','*.jpg','MultiSelect','on'); if ~FilterIndex return; end num_train = length(FileName); TrainData = zeros(num_train,16*16); TrainLabel = zeros(num_train,1); for k = 1:num_train pic = imread([PathName,FileName{k}]); pic = pic_preprocess(pic); % 将标准化图像按列拉成一个向量并转置，生成50*256的训练样本矩阵 TrainData(k,:) = double(pic(:)'); % 样本标签为样本所对应的数字 TrainLabel(k) = str2double(FileName{k}(4)); end %% 建立支持向量机 % [bestCVaccuracy,bestc,bestg] = ... % SVMcgForClass(TrainLabel,TrainData,-8,8,-8,8,10,0.8,0.8,4.5) % 设置GA相关参数 ga_option.maxgen = 100; ga_option.sizepop = 20; ga_option.cbound = [0,100]; ga_option.gbound = [0,100]; ga_option.v = 10; ga_option.ggap = 0.9; [bestCVaccuracy,bestc,bestg] = ... gaSVMcgForClass(TrainLabel,TrainData,ga_option) % 训练 cmd = ['-c ',num2str(bestc),' -g ',num2str(bestg)]; model = svmtrain(TrainLabel, TrainData, cmd); % 在训练集上查看识别能力 preTrainLabel = svmpredict(TrainLabel, TrainData, model); %% 载入测试样本 [FileName,PathName,FilterIndex] = uigetfile( ... {'*.jpg';'*.bmp'},'请导入测试图片','*.bmp','MultiSelect','on'); if ~FilterIndex return; end num_train = length(FileName); TestData = zeros(num_train,16*16); TestLabel = zeros(num_train,1); for k = 1:num_train pic = imread([PathName,FileName{k}]); pic = pic_preprocess(pic); TestData(k,:) = double(pic(:)'); TestLabel(k) = str2double(FileName{k}(4)); end %% 对测试样本进行分类 preTestLabel = svmpredict(TestLabel, TestData, model); assignin('base','TestLabel',TestLabel); assignin('base','preTestLabel',preTestLabel); TestLabel' preTestLabel' %% sub function of pre-processing pic function pic_preprocess = pic_preprocess(pic) % 图片预处理子函数 % 图像反色处理 pic = 255-pic; % 设定阈值，将反色图像转成二值图像 pic = im2bw(pic,0.4); % 查找数字上所有像素点的行标y和列标x [y,x] = find(pic == 1); % 截取包含完整数字的最小区域 pic_preprocess = pic(min(y):max(y), min(x):max(x)); % 将截取的包含完整数字的最小区域图像转成16*16的标准化图像 pic_preprocess = imresize(pic_preprocess,[16,16]);