手写体数字识别系统-基于特征提取识别

clc; clear all; close all; load Data.mat; [FileName,PathName,FilterIndex] = uigetfile({'*.jpg;*.tif;*.png;*.gif', ... '所有图像文件';... '*.*','所有文件' },'载入数字图像',... '.\\images\\手写数字\\t0.jpg'); if isequal(FileName, 0) || isequal(PathName, 0) return; end fileName = fullfile(PathName, FileName); I = imread(fileName); flag = 1; I1 = Normalize_Img(I); bw1 = Bw_Img(I1); bw2 = Thin_Img(bw1); bw = bw2; sz = size(bw); [r, c] = find(bw==1); /* 开发不易，整理也不易，如需要详细的说明文档和程序，以及完整的数据集，训练好的模型，或者进一步开发， 可加作者新联系方式咨询，WX：Q3101759565，QQ：3101759565 */ x2 = 1:sz(2); y2 = k2*(x2-pt2(1,1)) + pt2(1,2); if flag figure('Name', '数字识别', 'NumberTitle', 'Off', 'Units', 'Normalized', 'Position', [0.2 0.45 0.5 0.3]); subplot(2, 2, 1); imshow(I, []); title('原图像', 'FontWeight', 'Bold'); subplot(2, 2, 2); imshow(I1, []); title('归一化图像', 'FontWeight', 'Bold'); hold on; h = rectangle('Position', [rect(1:2)-1 rect(3:4)+2], 'EdgeColor', 'r', 'LineWidth', 2); xlabel('数字区域标记'); subplot(2, 2, 3); imshow(bw1, []); title('二值化图像', 'FontWeight', 'Bold'); subplot(2, 2, 4); imshow(bw, [], 'Border', 'Loose'); title('细化图像', 'FontWeight', 'Bold'); hold on; h = []; for i = 1 : length(hs) h = [h plot([1 sz(2)], [hs(i) hs(i)], 'r-')]; end for i = 1 : length(vs) h = [h plot([vs(i) vs(i)], [1 sz(1)], 'g-')]; end h = [h plot(x1, y1, 'y-')]; h = [h plot(x2, y2, 'm-')]; legend([h(1) h(4) h(7) h(8)], {'水平线', '竖直线', '左对角线', '右对角线'}, 'Location', 'BestOutside'); hold off; end v{1} = [1:sz(2); repmat(hs(1), 1, sz(2))]'; v{2} = [1:sz(2); repmat(hs(2), 1, sz(2))]'; v{3} = [1:sz(2); repmat(hs(3), 1, sz(2))]'; v{4} = [repmat(vs(1), 1, sz(1)); 1:sz(1)]'; v{5} = [repmat(vs(2), 1, sz(1)); 1:sz(1)]'; v{6} = [repmat(vs(3), 1, sz(1)); 1:sz(1)]'; v{7} = [x1; y1]'; v{8} = [x2; y2]'; for i = 1 : 8 num(i) = GetImgLinePts(bw, round(v{i})-1); end num(9) = sum(sum(endpoints(bw))); % 函数的Learning result = MaskRecon(Datas, num); msgbox(sprintf('识别结果:%d', result), '提示信息', 'modal');