MATLAB的实用字母识别系统实现含GUI界面

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();
    end
    templates = 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');
end

function 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

参考代码 用于字母识别实用的matlab程序 www.youwenfan.com/contentcsd/96848.html

建议优先使用HOG+SVM组合处理标准字体，对于复杂手写体可采用CNN方案。实际应用中需根据具体需求调整特征提取策略和分类器参数。