I cannot recognize character input image, using CNN and own characters image data-sets.

I had trained CNN by using own characters image datasets, whis is created by myself. And I follow the https://in.mathworks.com/help/deeplearning/examples/create-simple-deep-learning-network-for-classification.html . Upto this i gives more than 90% accuracy.

In the above mentionds process it just split into two from the datasets as training and testing.

But, I want to trained all the datasets without spliting and want to recognition the characters in an input images contained numbers words.

I tried to do but it gives wong results.

Code for training CNN:

close all;
clear all;
clc;
%%%%-------Load and Explore Image Data----%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

digitDatasetPath = fullfile('abcd');
imds = imageDatastore(digitDatasetPath, ...
    'IncludeSubfolders',true,'LabelSource','foldernames');
labelCount = countEachLabel(imds);
numTrainFiles = 180;
imdsTrain = splitEachLabel(imds,numTrainFiles,'randomize'); %%%%%% I Used all images for training
layers = [
    imageInputLayer([28 28 1])
    
    convolution2dLayer(3,8,'Padding','same')
    batchNormalizationLayer
    reluLayer
    
    maxPooling2dLayer(2,'Stride',2)
    
    convolution2dLayer(3,16,'Padding','same')
    batchNormalizationLayer
    reluLayer
    
    maxPooling2dLayer(2,'Stride',2)
    
    convolution2dLayer(3,32,'Padding','same')
    batchNormalizationLayer
    reluLayer
    fullyConnectedLayer(10)
    softmaxLayer
    classificationLayer];
    options = trainingOptions('sgdm', ...
    'InitialLearnRate',0.01, ...
    'MaxEpochs',4, ...
    'Shuffle','every-epoch', ...   
    'ValidationFrequency',30, ...
    'Verbose',false, ...
    'Plots','training-progress');
    mynet = trainNetwork(imdsTrain,layers,options);
    save mynet;

Program for Pre-Processing and Recognition:

close all;
clear all;
clc;

%outputfolder='L:\cnn';
load mynet

aa=imread('0.tif');
% figure;
% imshow(a);
% title('Input Image');

bb=rgb2gray(aa);
% figure;
% imshow(b);
% title('gray image');

cc=imbinarize(bb);
%figure;
%imshow(cc);
% title('binary Image');
dd=~cc;
% figure;
%imshow(dd);
[x,y]=size(cc);
ee=sum(cc,2);
mat2=y-ee;
mat3=mat2~=0;
mat4=diff(mat3);
index1=find(mat4);
[q,w]=size(index1);%size of index2 matrix is q*w
kap=1;
lam=1;
while kap<((q/2)+1)%number of loops=number of lines
    k=1;
    mat5=([]);
       for j=(index1(lam)+1):1:index1(lam+1)
         mat5(k,:)=cc(j,:); %store the line segmented matrix
           k=k+1;
       end
       lam=lam+2;   
       kap=kap+1;
       %figure, imshow(mat5);
       %imsave();
       
       
       %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
       %%%%%%%% WORD SEGMENT%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
       %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
       ccc=imbinarize(mat5);
       [xx,yy]=size(ccc);
       eee=sum(ccc,1);
       mat22=xx-eee;
       mat33=mat22~=0;
       mat44=diff(mat33);
       index11=find(mat44);
       [qq,ww]=size(index11);%size of index2 matrix is q*w
       kap1=1;
       lam1=1;
       while kap1<((ww/2)+1)%number of loops=number of lines
             kk=1;
             mat55=([]);
             for jj=(index11(lam1)+1):1:index11(lam1+1)
                 mat55(:,kk)=ccc(:,jj); %store the line segmented matrix
                 kk=kk+1;
             end
                lam1=lam1+2;   
                kap1=kap1+1;
                %figure, imshow(mat55);
                
                
                %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
                %%%%%%   REMOVE UNWANTED SPACE     %%%%%%%
                %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
                 invmat55=~mat55;
                 chasum=sum(invmat55,2);
                 measurements = regionprops(chasum == 0, 'Area', 'PixelIdxList');
                 fiveLongRegions = find([measurements.Area] >= 4);
                 theIndexes = vertcat(measurements(fiveLongRegions).PixelIdxList);
%                cccc=invmat55;
%                cccc(theIndexes,:)=1;
                 cccc=mat55;
                 cccc(theIndexes,:)=0;
                 bbbb=cccc;
                 %figure, imshow(bbbb);
                 
                 measurements = regionprops(bbbb, 'Area', 'BoundingBox');
                 allAreas = [measurements.Area];
                % Crop out each word
                a=001;
                for blob = 1 : length(measurements)
                      % Get the bounding box.
                      thisBoundingBox = measurements(blob).BoundingBox;
                      % Crop it out of the original gray scale image.
                      thisWord = imcrop(mat55, thisBoundingBox);
                      RI2 = imresize(thisWord,[28 28]);                      
                      YPred = classify(mynet,RI2);
                      figure;
                      imshow(RI2);
                      label = YPred;
                      title(string(label));
                     
                end
                
                
       end
       
end
    

I want my program into two part i.e., Traing and testing, as given bellow model: Test data input image contains numbers of lines and numbers of words.