How to do Image Segmentation on multiple digit?

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Muhamad Luqman Mohd Nasir
评论： DGM 2021-7-5
I want segment all the image into individual character and i manage to do so but the problem is with number 1 the final product is not what i want. Below is the image that i want to segment it and with the result:
This is the imagae after segmented witch is in the size of 28 by 28
As you can see digit 1 does not process correctly as this will affect CNN to recognize it.
So can anyone tell me how to solve this problem?
Below is the code:
%% Input Image
% Here input image which is a RGB image is preprocessed, converted to
% Binary Image, followed by noise removal
[filename,filepath]=uigetfile({'*.jpg'},'Select and image');
originalImage = imread(strcat(filepath, filename));
% Showing Original image
% figure(1);
imshow(originalImage);
title('Original Image');
% Conversion to grayScale image
grayImage = rgb2gray(originalImage);
% Conversion to binary image
threshold = graythresh(grayImage);
binaryImage = ~im2bw(grayImage,threshold);
% Removes all object containing fewer than 30 pixels
moddedImage = bwareaopen(binaryImage,30);
pause(1)
% Showing binary image
figure(2);
imshow(moddedImage);
title('Modified Image');
% Labelling connected components
[L,Ne] = bwlabel(moddedImage);
% Measuring properties of image regions
propied = regionprops(L,'BoundingBox');
hold on
% Plot Bounding Box
for n=1:size(propied,1)
rectangle('Position',propied(n).BoundingBox,'EdgeColor','g','LineWidth',2)
end
hold off
pause (1)
%% Image Segmentation
%figure; % Create a new figure window.
% Maximize the figure window.
%set(gcf, 'Units','Normalized','OuterPosition',[0 0 1 1]);
for n=1:Ne
[r,c] = find(L==n);
n1 = moddedImage(min(r):max(r),min(c):max(c));
n1 = imresize(n1,[128 128]);
n1 = imgaussfilt(double(n1),1);
n1 = padarray(imresize(n1,[20 20],'bicubic'),[4 4],0,'both');
subplot(3, 4, n);
imshow(n1);
segmentedImages1 = fullfile('segmentedImages1', sprintf('image%d.png', n));
imwrite(n1, segmentedImages1);
pause(1)
end
%% feed image
myFolder = 'D:\CNN test\segmentedImages1';
% Get a list of all files in the folder with the desired file name pattern.
filePattern = fullfile(myFolder, '*.jpg');
theFiles = dir(filePattern);
storedStructure = load('test2.mat');
net = storedStructure.net;
%i = 0;
%outcome = zeros(1,1);
%outcome = [];
for k = 2 : length(theFiles)
baseFileName = theFiles(k).name;
fullFileName = fullfile(theFiles(k).folder, baseFileName);
fprintf(1, 'Now reading %s\n', fullFileName);
I = imread(fullFileName);
subplot(3, 4, k);
imshow(I); % Display image.
drawnow; % Force display to update immediately.
label=(classify(net,I));
title([' Recognized Digit is ' char(label)])
fprintf( 'student id: %s\n',label)
end
%% Displaying Detected Text
fprintf( 'student id: %s\n',label)
  3 个评论
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Muhamad Luqman Mohd Nasir
Muhamad Luqman Mohd Nasir 2021-7-5
@Image Analyst Hi Sir its seems that the file is to large to be attach to the comment even after i zip the file. But here is the script of the file. It is bassically a script to train CNN
%CNN program
digitDatasetPath='D:\CNN test\mnisdigitdataset';
digitimages=imageDatastore(digitDatasetPath,'IncludeSubfolders',true,'LabelSource','foldernames');
numTrainFiles=750; %numTrainFiles=0.75 (75%)
[TrainImages,TestImages]=splitEachLabel(digitimages,numTrainFiles,'randomize');
%Building CNN
layers=[
imageInputLayer([28 28 1],'Name','Input')
convolution2dLayer(3,8,'Padding','same','Name','Conv_1')
batchNormalizationLayer('Name','BN_1')
reluLayer('Name','Relu_1')
maxPooling2dLayer(2,'Stride',2,'Name','MaxPool_1')
convolution2dLayer(3,16,'Padding','same','Name','Conv_2')
batchNormalizationLayer('Name','BN_2')
reluLayer('Name','Relu_2')
maxPooling2dLayer(2,'Stride',2,'Name','MaxPool_2')
convolution2dLayer(3,32,'Padding','same','Name','Conv_3')
batchNormalizationLayer('Name','BN_3')
reluLayer('Name','Relu_3')
maxPooling2dLayer(2,'Stride',2,'Name','Maxpool_3')
convolution2dLayer(3,64,'Padding','same','Name','Conv_4')
batchNormalizationLayer('Name','BN_4')
reluLayer('Name','Relu_4')
fullyConnectedLayer(10,'Name','FC')
softmaxLayer('Name','Softmax');
classificationLayer('Name','Output Classification');
];
%Igraph = layerGraph(layers);
%plot(Igraph); %Plotting Network Structure
%-----------------------------------Training Options-----------------
options = trainingOptions('sgdm','InitialLearnRate',0.01,'MaxEpochs',4,'Shuffle','every-epoch','ValidationData',TestImages,'ValidationFrequency',30,'Verbose',false,'Plots','training-progress');
net = trainNetwork(TrainImages,layers,options); %Network Training
Ypred = classify(net,TestImages); %Recognizing Digits
YValidation = TestImages.Labels; %Getting Labels
accuracy = sum(Ypred == YValidation)/numel(YValidation); %Finding %age accuracy
Muhamad Luqman Mohd Nasir
Muhamad Luqman Mohd Nasir 2021-7-5
@Image Analyst Hello Sir do you any other suggestion that i can do to solve this problem? Sorry if i have taken your time with my problems.

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采纳的回答

DGM
DGM 2021-7-4
Don't rescale the characters in a way that changes their aspect ratio. Resize based only on one dimension, then make up for it when you pad.
minpadsize = 4;
outsize = [28 28];
nr = imresize(n1,[outsize(1)-2*minpadsize NaN],'bicubic');
hpad = (outsize(2)-size(nr,2))/2;
np = padarray(nr,[minpadsize floor(hpad)],0,'pre');
np = padarray(np,[minpadsize ceil(hpad)],0,'post');
  4 个评论
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Muhamad Luqman Mohd Nasir
Muhamad Luqman Mohd Nasir 2021-7-5
Well i gues you are right, the script you gave solve the problem and work wonders, maybe i should post another question regarding the CNN.Thank You Sir @DGM
DGM
DGM 2021-7-5
What I meant is to isolate the code doing the image transformation and make sure it works by itself.

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