Recovery of Drawing Order of Handwritten Image

The following code gives an overview (Ask for full source code of application) to compute static and dynamic features of scanned handwritten image. The dynamic features compute drawing order of a handwritten image as shown in following figure. The code is written in Matlab.

The following function feature_extraction calls static and dynamic features and perform necessary preprocessing.

function v = feature_extraction(im)

    v=[];

    im=imresize(im,[100 100]); im=im2bw(im);

    v=[v more_features(im)];  %%% static features computation, function written below

    im2=im;

    %%%% for thin image 

    fix_no=63; cch_length=12; path=[]; list=[];

    im=bwmorph(im,'thin',Inf); im=bwmorph(im,'spur',3);

    im1=regionprops(im,'PixelList','Extrema','Area');

    for i=1:length(im1)

        list1=im1(i).PixelList;

        P1=ceil(im1(i).Extrema(1,:)); %P=P1;

        P=one_ngh(list1,P1); %find one-ngh point close to (0,0)

        x=P(1,1); y=P(1,2); path=[x y];

                path=tr_list(list1,x,y,path);  %traverse list as recover drawing order of skeleton image, function given below

        list=[list; path];

    end

    [m n]=size(list);list=resample(list,fix_no,m); %to resample points   

    list=list(3:end,:);

    ccpath=list_path(list,cch_length); v=[v ccpath];

    %%%%%canny chain code

    im=im2; fix_no=133; cch_length=12; path=[]; list=[];

    im=edge(im,'canny'); 

    im1=regionprops(im,'PixelList','Extrema','Area');

    for i=1:length(im1)

        list1=im1(i).PixelList;

        P1=ceil(im1(i).Extrema(1,:)); %P=P1;

        P=one_ngh(list1,P1); %find one-ngh point close to (0,0)

        x=P(1,1); y=P(1,2); path=[x y];

        path=tr_list(list1,x,y,path);  %traverse list as recover drawing order of boundary pixels image, function given below

        list=[list; path];

    end

    [m n]=size(list); list=resample(list,fix_no,m); %to resample points   

    list=list(3:end,:); list=[list;list(1,:)];

    ccpath=list_path(list,cch_length); v=[v ccpath];      

end

Static Features (compute pixels density in squares, horizontal, vertical and diagonal styles)

function v = more_features(im)

   im=im_crop(im);

   im=imresize(im,[100 100]);

   [m n]=size(im);    

   t=10; 

   k=m/t; part=m/k; im2=[]; v=[]; r=1; c=1;

   for i=1:part

        for j=1:part

            im2=im(r:(r+k-1),c:(c+k-1));

            v=[v nnz(im2)];

            c=c+k;

        end

        r=r+k; c=1;

   end   

   ub=100; f=9;

   for i=1:10:ub

       v=[v nnz(im(i:i+9,:))];

       v=[v nnz(im(:,i:i+9))];

   end

   %}

   ub=100; f=9; v=[];

   for i=1:10:ub

       s=0; m=[];

       for j=i:1:i+f

           s=s+nnz(diag(im,j));

       end

       v=[v s];

   end

   for i=-1:-10:-ub

       s=0; m=[];

       for j=-i:-1:-(i+f)

           s=s+nnz(diag(im,j));

       end

       v=[v s];

   end

   im=fliplr(im); [m n]=size(im);

   for i=1:10:ub

       s=0; m=[];

       for j=i:1:i+f

           s=s+nnz(diag(im,j));

       end

       v=[v s];

   end

   for i=-1:-10:-ub

       s=0; m=[];

       for j=-i:-1:-(i+f)

           s=s+nnz(diag(im,j));

       end

       v=[v s];

   end

end

Dynamic Features (Recover drawing order through skeleton and boundary pixels of image, its a recursive program)

function path=tr_list(list,x,y,path)

    [m n]=size(path); [m1 n1]=size(list);

    if m>=m1

        return;

    end

    ngh=tr_ngh(x,y,path,list); [m1 n1]=size(path); [m2 n2]=size(ngh);

    %

    if m1>1 && m2>1

        pp2(1,1)=x; pp2(1,2)=y; pp1(1,1)=path(end-1,1); pp1(1,2)=path(end-1,2);

        ngh=tr_prev_list(ngh,pp1,pp2);

    end

    %

    [m n]=size(ngh);

    for i=1:m

        P2=ngh(i,:);

        checkp=is_member(P2,path);

        if checkp==0

            path=[path;P2];

        end

        x=P2(1,1); y=P2(1,2);

        path=tr_list(list,x,y,path);

    end

end