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MATLAB的实用字母识别系统实现含GUI界面

news 2025/8/19 11:01:36

一、代码

%% 主程序（letter_recognition_gui.m）
function letter_recognition_gui% 创建GUI界面fig = figure('Name','字母识别系统','NumberTitle','off',...'Position',[100,100,800,600],'MenuBar','none','Resize','off');% 控件布局uicontrol('Style','text','String','输入方式:','Position',[20,550,80,20]);uicontrol('Style','popupmenu','String',{'手写输入','图片上传'},...'Position',[120,550,150,20],'Tag','InputMode');% 绘图区域ax = axes('Units','pixels','Position',[50,150,300,300],...'XTick',[],'YTick',[],'Box','on');uicontrol('Style','pushbutton','String','清除画布','Position',[370,400,100,30],...'Callback',@clearCanvas);% 控制按钮uicontrol('Style','pushbutton','String','开始识别','Position',[600,400,100,30],...'Callback',@recognizeLetter);uicontrol('Style','pushbutton','String','加载模板','Position',[600,350,100,30],...'Callback',@loadTemplates);% 结果显示uicontrol('Style','text','String','识别结果:','Position',[50,450,80,20]);resultText = uicontrol('Style','text','String','',...'Position',[150,450,200,20],'Tag','Result');% 数据存储setappdata(fig,'canvas',[]);setappdata(fig,'templates',[]);
end%% 清除画布函数
function clearCanvas(~,~)cla(gca);axis([0 1 0 1]);set(gca,'XTick',[],'YTick',[],'Box','on');setappdata(gcf,'canvas',[]);set(findobj(gcf,'Tag','Result'),'String','');
end%% 识别函数
function recognizeLetter(~,~)canvas = getappdata(gcf,'canvas');if isempty(canvas)errordlg('请先绘制或加载字母!','错误');return;end% 预处理img = imresize(canvas, [28 28]);img = imbinarize(rgb2gray(img));img = double(img(:)) / 255;% 特征提取（HOG特征）features = extractHOGFeatures(img', 'CellSize',[4 4]);% 分类识别if isempty(getappdata(gcf,'templates'))loadTemplates();endtemplates = getappdata(gcf,'templates');% 使用SVM分类器predictedLabel = predict(templates.svmModel, features);predictedChar = char(predictedLabel + 'A' - 1);% 显示结果set(findobj(gcf,'Tag','Result'),'String',predictedChar);
end%% 加载模板函数
function loadTemplates(~,~)% 加载预训练模板（示例）templates.data = load('letter_templates.mat'); % 包含训练好的SVM模型setappdata(gcf,'templates',templates);
end

二、关键功能

1. 图像预处理流程

% 灰度转换
grayImg = rgb2gray(inputImg);% 自适应二值化
binaryImg = imbinarize(grayImg, 'adaptive');% 形态学处理
cleanImg = bwareaopen(binaryImg, 50);
cleanImg = imclose(cleanImg, strel('disk',2));% 边缘检测
edges = edge(cleanImg, 'Canny');

2. 特征提取实现

% HOG特征提取
[hogFeatures, hogVisual] = extractHOGFeatures(cleanImg,...'CellSize',[4 4], 'BlockSize',[2 2], 'NumBins',9);% 方向场编码
[dx, dy] = gradient(double(cleanImg));
orientation = atan2(dy, dx);

3. 分类器训练代码

% 加载训练数据
[XTrain, YTrain] = loadTrainingData();% 数据划分
cv = cvpartition(YTrain,'HoldOut',0.3);% 训练SVM模型
svmModel = fitcsvm(XTrain(training(cv),:), YTrain(training(cv)),...'KernelFunction','rbf','Standardize',true);% 保存模型
save('letter_templates.mat','svmModel');

三、GUI界面增强功能

1. 手写输入交互

% 鼠标事件回调
function mousePress(~,event)axes(handles.canvasAxes);hold on;currentPoint = get(gca,'CurrentPoint');startPoint = currentPoint(1,1:2);line([startPoint(1) startPoint(1)],[startPoint(2) startPoint(2)],'Color','r');
endfunction mouseMove(~,event)axes(handles.canvasAxes);currentPoint = get(gca,'CurrentPoint');endPoint = currentPoint(1,1:2);line([prevX endPoint(1)],[prevY endPoint(2)],'Color','r','LineWidth',2);prevX = endPoint(1);prevY = endPoint(2);
end

2. 结果可视化

% 绘制特征图
function plotFeatures(features)figure;subplot(2,2,1);imagesc(reshape(features(1:64),8,8));title('原始特征');subplot(2,2,2);bar(features(65:73));title('HOG方向直方图');subplot(2,2,3);imshow(imresize(canvas,0.5));title('输入图像');subplot(2,2,4);plotConfusionMatrix(confusionMat);
end