require a code for automatic licencse plate recognition of vehicles.

function  [LPImageGray,LPImageBW,BW2,BW3,stats,LPImageTH,EdgeImage,imrefdata,ids,im] = AnprEngine( LPImage,thresh)
%ANPRENGINE Summary of this function goes here
%   Process image input analytics and output licence plate alphanumeric string
%   Based on static image
tic
%%
% Variables Initialization
% EligibleBands = zeros(3,2);
% Pointer = 1;
% Sensing = 0;
% MinimalHeight = 15;
srcData = load('AnprSysData.mat');
imrefdata = srcData.AlphaNumRef;
im = [];
C = zeros(109);
% toc
%%
% Pre-process LPImage
LPImageGray = rgb2gray(LPImage);
LPImageTH = imtophat(LPImageGray,strel('ball',12,7));%Close ball 18 7/ Far ball 12 4 
imadjust(LPImageTH,[0;0.1],[0;1]);
%[EdgeImage, thr, gv, gh] = edge(LPImageGray,'sobel','nothinning');
[EdgeImage, ~, ~] = edge(LPImageGray,'sobel');
LPImageBW = ~im2bw(LPImageTH,thresh);
%LPImageVH = edge(LPImageGray, 'sobel',0.11,'vertical');
%sz = size(LPImage);
toc
%%
% Skew detection Spacial Transformation correction

%%
% Process Image ROI candidates, Heuristic and Bands Elimamination
toc
ProjectionVH = sum(LPImageVH,2);%Compute sum of Horizontal Projection of vertical lines
maxIndexValue = find(ProjectionVH == max(ProjectionVH), 1,'last');%Find Max Index Value of ProjectionVH 1*m Matrix
% Locate most significative eligible bands.
sensor = mean(ProjectionVH);% Define standard deviation of ProjectionVH to find eligible bands 
for idx = 1:sz(1,1)
    if(ProjectionVH(idx) > sensor && Sensing == 0)
        Sensing = 1;
        EligibleBands(Pointer, 1) = idx;
    end
    if(ProjectionVH(idx) < sensor && Sensing == 1)
        Sensing = 0;
        EligibleBands(Pointer, 2) = idx;
        if((EligibleBands(Pointer, 2)-EligibleBands(Pointer, 1))>MinimalHeight)
        Pointer = Pointer + 1;
        end
    end
end
for i = 1:sz(1,1)
    if(i < EligibleBands(1,1) || i > EligibleBands(1,2))
        LPImageBW(i,:) = 0;
    end
end

%%
% Locate Licence Plate Bands

%%
% Blobs Analysis
CC = bwconncomp(LPImageBW);
stats = regionprops(CC, 'Area','BoundingBox','Image');
ids = find([stats.Area] > 8 & [stats.Area] < 250);
BW2 = ismember(labelmatrix(CC),ids);
toc
%%
% Blobs Analysis
CC2 = bwconncomp(~LPImageBW);
stats2 = regionprops(CC2,'Area','BoundingBox','Eccentricity');
ids2 = find([stats2.Area] > 20 & [stats2.Eccentricity] > 0.95);
BW3 = ismember(labelmatrix(CC2),ids2);
%BW3 = [];% ismember(labelmatrix(CC2),ids2);
toc
%%
% Pre-OCR
for k=1:length(ids)
    imOriginal =  stats(ids(k)).Image;
    BB = stats(ids(k)).BoundingBox;
    NormIm{1,1} =  resizem(imOriginal,[60,30]);
    NormIm{2,1} =  BB(4)/BB(3);
    [NormIm{3,1} NormIm{4,1}, NormIm{5,1}] = getMaxCorrelationRate(NormIm{1,1},imrefdata);
    if(BB(4)/BB(3)>=1.4 && BB(4)/BB(3)<=2 || BB(4)/BB(3)>=3 && BB(4)/BB(3)<=10)
        im = [im NormIm];

      %imshow(cell2mat(PD(1,:))) %to display all column in first row on NormIm
      %output
      %imshow(cell2mat(E2(2)))
      end
  end
  toc
  %%
  % OCR Function 
      function [Rate,id, Letter] = getMaxCorrelationRate(imOriginal,imrefdata)
              for n=1:length(imrefdata)
                  Rates(n) = corr2(imOriginal,cell2mat(imrefdata(1,n)));
              end
              [Rate,id] = max(Rates);
              if(Rate>0.48)
                  Letter = imrefdata{2,id};
              else
                  Letter ='';
              end;
      end
  %%
  % OCR AlphaNumerical resolution

%%
% LPNumber = 'Hello Plate';
%% Output
end
% It uses correlation instead of ANN. 
% All is in AnprSysData.mat file Chars are indexed from 1 to 109

% Author Mahjoub El attar 2002-2006 for smartKam. For more information feel free to contact me at my profile email contact address.

fi = imread('noplate.jpg');

%imshow(fi)

fin = rgb2gray(fi);

imshow(fin);

d=double(fin)

%imshow(fin)

[r c]= size(d)

% Mexican filter operator

filter = [ 0 0 0 -1 -1 -1 0 0 0 ; 0 -1 -1 -3 -3 -3 -1 -1 0; 0 -1 -3 -3 -1 -3 -3 -1 0; -1 -3 -3 6 13 6 -3 -3 -1; -1 -3 -1 13 24 13 -1 -3 -1; -1 -3 -3 -6 13 6 -3 -3 -1; 0 -1 -3 -3 -1 -3 -3 -1 0; 0 -1 -1 -3 -3 -3 -1 -1 0; 0 0 0 -1 -1 -1 0 0 0 ];

        % creating image matrix for mexican hat operator

        gm = zeros(r,c);

        for i=5:2:r-5

            for j=5:2:c-5

                gm(i,j) = sum(sum(double(fin(i-4:i+4,j-4:j+4)).*filter,2));

            end;

        end;

        % removing the unwanted edges by using a threshold

        fh = gm>1200;

        %Dilation operation 

        x = 1;

        y =1;

        fs = double(fh);

        se = ones(3,3);

        for x= 3:3:r-20

            for y = 3:3:c-20

                if(x+50<=r)

                    xend = x+50;

                else

                    xend = r;

                end;

                if(y+100<=r)

                    yend = y + 150;

                else

                    yend = c;

                end;

                if(sum(fh(x:xend,y))<=35||sum (fh(x,y:yend,2)<=60))

                    if(sum(fh(x,y:y+3),2)<=3) &&  (sum(fh(x,y:y+3),2)>2)

                        fs(x-2:x+2,y-2:y+2)=bwmorph(fh(x-2:x+2,y-2:y+2),'dilate',se);

                    end;

                end;

            end;

        end;

        %imshow(fin) 

%image with dilation performed

f=double(fs);

[row col]=size(f);

%initialising a matrix for a segmented image

g=zeros(row,col);

gl=zeros(row,col);

label=1;

n=1;

x=1;

iter=[];

it=0;

ss_prev=0;

nn=[];

sss_mat=[];

for i=1:2:row

      for j=1:2:col

          r_pt=i;

          c_pt=j;

          if(g(r_pt,c_pt)==0)

              while(true)

%using 4 neighbour rule

if(f(r_pt(n),c_pt(n))==1 && g(r_pt(n),c_pt(n))==0)

      g(r_pt(n),c_pt(n))=label;

      if(r_pt(n)+1<=row)

          if(f(r_pt(n)+1,c_pt(n))==1)

              r_pt=[r_pt r_pt(n)+1];

              c_pt=[c_pt c_pt(n)];

              x=x+1;

          end;

      end;

      if(c_pt(n)-1>=1)

          if(f(r_pt(n),c_pt(n)-1)==1)

              r_pt=[r_pt r_pt(n)];

              c_pt=[c_pt c_pt(n)-1];

              x=x+1;

          end;

      end;

      if(c_pt(n)+1<=col)

          if(f(r_pt(n),c_pt(n)+1)==1)

              r_pt=[r_pt r_pt(n)];

              c_pt=[c_pt c_pt(n)+1];

              x=x+1;

          end;

      end;

      if(r_pt(n)-1>=1)

          if(f(r_pt(n)-1,c_pt(n))==1)

              r_pt=[r_pt r_pt(n)-1];

              c_pt=[c_pt c_pt(n)];

              x=x+1;

          end;

      end;

end;

                   if(n>=x)

                       break;

                   end;

                   n=n+1;

          end;

          y1=min(r_pt);

          y2=max(r_pt);

          x1=min(c_pt);

          x2=max(c_pt);

          a1=g(min(r_pt):max(r_pt),min(c_pt):max(c_pt));

          f1=d(min(r_pt):max(r_pt),min(c_pt):max(c_pt));

          [ra ca]=size(a1);

        |  if(n>=50)|

              b1=bwlabel(a1);

              ss=regionprops(b1,'euler number');

              sss=struct2array(ss);

              sss=min(sss);

              sss_mat=[sss_mat sss];

              if(sss<ss_prev && sss<0 && ca <=190 && ra<=60 && ca>=50 && ra >=15 && mean(mean(f1))<=220)

                  x_cor1=x1;

                  y_cor1=y1;

                  x_cor2=x2;

                  y_cor2=y2;

                  ss_prev=sss;

              end;

          label=label+1;

          else

              g(r_pt,c_pt)=0;

          end;
      end;

      x=1;

      n=1;

      it=1;

end;

end;

if(exist('y_cor1')==1)

      d(y_cor1:y_cor1+2,x_cor1:x_cor2)=255;

      d(y_cor2:y_cor2+2,x_cor1:x_cor2)=255;

      d(y_cor1:y_cor2,x_cor1:x_cor1+2)=255;

      d(y_cor1:y_cor2,x_cor2:x_cor2+2)=255;

end;

% Segmented licence plate image

d=mat2gray(d);

lp=d(y_cor1:y_cor2,x_cor1:x_cor2);

%%% 2. Character Segmentation

%License plate image, characters of wcich are to be segmented

lp1 = d(y_cor1:y_cor2,x_cor1:x_cor2);

[rl cl] = size(lp1);

% Median Filtering

lp = medfilt2(lp1,[3 3]);

% Contrast Enhancement

lpf = imadjust(lp,stretchlim(lp,[0.1 0.5]));

%creating output image matrix

output= zeros(rl,cl);

% Window for local threshold operation

dis = round(cl/7);

% Local threshold operation

for i=1:dis:cl

      if(i+dis-1<=cl)

          t=threshcal(lpf(:,i:i+dis-1),a);

          for i=1:dis:cl

              if(i+dis-1<=cl)

                  t=threshcal(lpf(:,i:i+dis-1),a);

                  output(:,i:i+dis-1)=lpf(:,i:i+dis-1)<=t;

              else

                  t=threshcal(lpf(:,i:cl),a);

                  for z1=2:rl-1

                      for z2=i+5:cl-5

                          if(mean(mean(lpf(z1-1:z1+1,z2-5:z2+5)))<=t)

                              output(z1,z2)=1;

                          end;

                      end;

                  end;

                  output(:,i:cl)=lpf(:,i:cl)<=t;

              end;

          end;

      end;

end;

          % Structuring element for erosion operation

          se = [1 1 1  1 1 1 1 1 1 1 1 1  1 1 1]

          output = output - imerode(output,se);

          [of lab lpdet] = reggrowl(logical(output),number);

          % Segmented characters

          lpdet = logical(lpdet);

          % Character Recognition

          % output String giving licence plate information

          lpstr=[];

          for i= 1:lab-1

           R = lpdet(:,st:st+9);

           st = st+10;

           b = bwlabel(R);

           % Feature extraction

           ar = struct2array(regionprops(b,'area'));

           or = struct2aarray(regionprops(b,'orientation'))/90;

           eu = struct2array(regionprops(b,'eulernumber'))/10;

           pe = struct2array(regionprops(b,'perimeter'));

           mi = struct2array(regionprops(b,'minoraxislength'));

           ma = struct2array(regionprops(b,'majoraxislength'));

           temp = logical(R);

           % Reflection X and Y coefficient determination

           v1 = temp;

           v1(:,6:10)=flipdim(temp(:,1:5),2);

           vx = (v1 + temp)/2;

           vx = vx>=0.5;

           xcoef = sum(sum(temp),2)/sum(sum(vx),2);

           v2 = temp;

           v2(1:12,:) = flipdim(temp(13:24,:),1);

           vy = (v2 + temp)/2;

           vy = vy >= 0.5;

           ycoef = sum(sum(temp),2)/sum(sum(vy),2);

           ed = struct2array(regionprops(b,'equivdiameter'))/100;

           [val pos] = max(fa);

           vcoeff = pe(pos)/ar(pos);

           mcoeff = ed(pos);

           Rp = [xcoef/ycoef;pe(pos)/ar(pos);mi(pos)/ma(pos)];

           answer=find(compet(A2)==1);

           if(i<=numel(lpnum))

           if(alphamat(answer)==lpnum(i))

               numrc = numrc+1;

           else

               answ = find(alphamat==lpnum(i));

               err(answ) = err(answ) + 1;

           end;

           end;

           lpstr = [lpstr alphamat(answer)];

          end;

          numc = numc + numel(lpnum);

          if(strcmp(lpstr,lpnum)==1)

              tr = tr + 1;

              sr = strcat(num2str(num),'/',num2str(1),'//');

              casep = [casep sr];

          else

              fr = fr +1;

              sr = strcat(num2str(num),'/',num2str(1),'/',num2str(answer),'//');

              casen = [casen sr];

          end;
%% Now your code is a little more clear....

Hi, You must use it as this. A = AnprEngine(Image, 0.17); Ok.... let me zip all files with the User-Interface "GUI" and testing snapshots.