image inpainting

I am student of M.E.(I.T.). i have a research topic is "image inpainting(text removal)". my task is to remove superimpoed text from images. my first task is to extract text from images. i have succussfully extract text from the images. so my problem is how can i remove that text from images. is there any method??? can u please suggest me???

thanks and regards,

uday modha M.E.(I.T)

[Chandra Kurniawan]

Hi, Uday

Please show me the image and the code you used to extract the text!

[uday modha]

sir i m using the "morphological text extraction from images" algorithms given by hasan and karam.

[Chandra Kurniawan]

Yes, please show me the image and the code.

http://www.mathworks.com/matlabcentral/answers/7924-where-can-i-upload-images-and-files-for-use-on-matlab-answers

[uday modha]

sir can i upload files to flicker??

[Chandra Kurniawan]

Upload the image to flicker, then post your code in this page.

[uday modha]

sir below is my sample image link

http://www.flickr.com/photos/document1984/?saved=1

and here is the complete code

==========================================================

% EE368 Final Project Spring 2010-11

% Text Extraction Routine

% Author: Uday Modha

% Version: 7

% Dec 25,2011

% Situations that the code does not work well:

% - Text too large (average letter size > 0.3 image size)

% - Text too small (letter height < 15 px)

% - Largest text vs. smallest text ratio too high

% - Connected letters (not good for OCR either)

% - Low contrast with the background (unable to detect the edges of the

% letters

% - Noisy background (Too many noisy edges deteced)

% - prone to detect text located around the center of the image

%function I_out = textExt_1(I_in)

%clear all; clc; close all;

%for testI = 1:32

%t1 = cputime; % save cpu time

tic;

%fileName = strcat('test_a_',num2str(testI,'%#02i'));

%fileName = 'test_a_07';

%fileName = 'test6';

%loadName = strcat('rawImages/',fileName,'.jpg');

loadName='123.jpg';

I_in = imread(loadName);

% Input should be a 2D grayscale level image or 3D RGB image

dim_I = length(size(I_in));

if ((dim_I > 3) || (dim_I < 2))

fprintf('\nError! Input Input should be a 2D grayscale level image or 3D RGB image\n');

return;

end

% Initialization

%I_out = uint8(zeros(size(I_in))); % init output

% Calculate Intensity Image Y

% Y = 0.299R + 0.587G + 0.114B

if (dim_I == 3)

% if image is in RGB form

%Y = 0.299*double(I_in(:,:,1)) + 0.587*double(I_in(:,:,2)) + 0.114*double(I_in(:,:,3));

Y = 0.299*I_in(:,:,1)+0.587*I_in(:,:,2)+0.114*I_in(:,:,3);

%Y = rgb2gray(I_in);

uday=input('rgb');

else

% if image is grayscale

Y = I_in;

end

dsRate = 3;

% Downsampling

N = downsample(Y,dsRate);

N = N';

P = downsample(N,dsRate);

Y1 = P';

clear N P;

%edge0 = edge(Y1,'sobel',0.01);

%edge0 = edge(Y1,'prewitt');

%edge0 = edge(Y1,'log');

%edge0 = edge(Y1,'roberts');

%edge0 = edge(Y1,'canny');

% Increase contrast

Y1a = (double(Y1) / double(max(Y1(:)))).^1.5;

Y1a = uint8(Y1a / max(Y1a(:)) * 255);

%Y1 = uint8(double(Y1) / double(max(Y1(:))) * 255);

% Y is blurred using open-closed / closed-open filters

% (to reduced false edges and over-segmentation)

SE = strel('arbitrary', ones(3,3));

%Y_bl = (imclose(imopen(Y1,SE),SE) + imopen(imclose(Y1,SE),SE))/2;

Y_bl = (imclose(imopen(Y1a,SE),SE) + imopen(imclose(Y1a,SE),SE))/2;

% The Morphological gradient (MG) operator is applied to the blurred image

% Y_bl resulting in an image e_s

e_s = imdilate(Y_bl,SE) - imerode(Y_bl,SE);

% e_s is then thresholded to obtain a binary edge image

g1 = [-1 0 1]; g2 = [-1;0;1];

s = uint8(max(abs(conv2(double(e_s),double(g1),'same')),abs(conv2(double(e_s),double(g2),'same'))));

gamma = double(sum(sum(s.*e_s)))/double(sum(s(:)));

%fprintf('\ngamma = %8.3f\n',gamma);

edge0 = im2bw(e_s,gamma/double(max(e_s(:))));

%clear SE Y_bl e_s;

fprintf('\nProcessing Time: %8.3f sec\n',toc);

% A close operation before analyze the regions

edge1 = imclose(edge0,ones(3,3));

%edge1 = edge0;

%clear edge0;

% Regionprops operations

[L1 num1] = bwlabel(edge1,4);

R1 = regionprops(L1,'BoundingBox','Area');

%Major1 = regionprops(L1,'MajorAxisLength');

%Minor1 = regionprops(L1,'MinorAxisLength');

[pixNum_h ,pixNum_w] = size(edge1);

edge_m = zeros(pixNum_h, pixNum_w);

S = [];

fprintf('before loop %d',num1);

%num1=1;

for i = 1:num1

fprintf('entering into loop');

% box = R1(i).BoundingBox;

%area = R1(i).Area;

box = R1(i).BoundingBox;

area = R1(i).Area;

box_w = box(3);

box_h = box(4);

box_ratio = box_w / box_h;

area_ratio = area / box_w / box_h;

center_w = (box(1) + box(3)/2)/pixNum_w;

center_h = (box(2) + box(4)/2)/pixNum_h;

fprintf('\nwidth: %d\n',center_w);

fprintf('\nheight: %d\n',center_h);

%{

major = Major1(i).MajorAxisLength;

minor = Minor1(i).MinorAxisLength;

if (major / minor > 6)

continue;

end

%}

if ((center_w>0.15)&&(center_w<0.85)&&(center_h>0.2)&&(center_h<0.8)) % position filter

%if ((center_h>0.2)&&(center_h<0.8)) % position filter

if ((box_ratio<10)&&(box_w<0.5*pixNum_w)&&(box_w>10))

if ((box_h<0.3*pixNum_h)&&(box_h>15))

if (area_ratio>0.1)% geometry filter

%edge_m(L1==i) = 1;

edge_m(uint16(box(2):(box(2)+box(4))),uint16(box(1):(box(1)+box(3)))) = 1;

fprintf('\nnumber: %d\n',i);

S = [S ; box i]; % save bounding box information of the candidates

end

end

end

end

end

%clear Bounding1 Area1;

fprintf('hellow how r u?');

if size(S,1) < 1

break;

end

mean_h1 = mean(S(:,4)); % the mean height of all boxes

std_h1 = std(S(:,4)); % the standard deviation

for i = 1:size(S,1)

if (S(i,4) < mean_h1*0.6)||(S(i,4) > mean_h1*1.8)

% remove candidate from mask if the height is beyond mean +/- 2*std

edge_m(uint16(S(i,2):(S(i,2)+S(i,4))),uint16(S(i,1):(S(i,1)+S(i,3)))) = 0;

end

end

edge_m2 = imclose(edge_m, ones(1,1.5*uint16(mean_h1)));

% Analyze word candidates

[L3 ,num3] = bwlabel(edge_m2,4);

R3 = regionprops(L3,'BoundingBox','Area');

Y2 = false(size(Y));

sum_w3 = 0;

sum_h3 = 0;

for i = 1:num3

box = R3(i).BoundingBox;

sum_w3 = sum_w3 + box(3);

sum_h3 = sum_h3 + box(4);

end

mean_w3 = double(sum_w3) / num3;

mean_h3 = double(sum_h3) / num3;

for i = 1:num3

box = R3(i).BoundingBox;

area = R3(i).Area;

% if width is not like other texts in the image, skip

if (box(3) < mean_w3/3)

continue;

end

%{

% if height is not like other texts in the image, skip

if (box(4) < mean_h3/3)

continue;

end

%}

% if width / height is too small, not likely to be text, skip

if (box(3)/box(4) < 1)

continue;

end

% if area ratio is too small, not likely to be text, skip

if (area / box(3) /box(4) < 0.6)

continue;

end

h_low = dsRate*(max(uint16(box(2)),1)-1)+1;

h_high = dsRate*min(uint16(box(2)+box(4)),pixNum_h);

w_low = dsRate*(max(uint16(box(1)),1)-1)+1;

w_high = dsRate*min(uint16(box(1)+box(3)),pixNum_w);

word_a = Y(h_low:h_high,w_low:w_high);

thresh = graythresh(word_a);

% TRY TO ADD LOCAL THRESHOLD HERE!!!!!

% TRY TO ADD LOCAL THRESHOLD HERE!!!!!

varThresh = 30;

numInX = 5;

numInY = 1;

globalThresh = 255 * graythresh(word_a);

windowSizeX = floor(size(word_a,2)/numInX);

windowSizeY = floor(size(word_a,1)/numInY);

%I_t = uint8(size(word_a));

I_t = uint8(zeros(numInY,numInX));

for j = 1: numInX

if (j == numInX)

x1 = (numInX -1) * windowSizeX + 1;

x2 = size(word_a,2);

else

x1 = (j-1) * windowSizeX + 1;

x2 = j * windowSizeX;

end

for k = 1: numInY

if (k == numInY)

y1 = (k-1) * windowSizeY + 1;

y2 = k * windowSizeY;

else

y1 = (k-1) * windowSizeY + 1;

y2 = size(word_a,1);

end

localI = word_a(y1:y2,x1:x2);

% If variance greater than a certain value

% Do Otsu's method

if var(double(localI(:))) > varThresh

level = graythresh(localI(:));

%I_t(y1:y2,x1:x2) = 255 * level;

I_t(k,j) = 255 * level;

% Else, consider the region as uniform forground / background

else

%I_t(y1:y2,x1:x2) = globalThresh;

I_t(k,j) = 100;

end

end

end

I_t_r = imresize(I_t,size(word_a),'bilinear');

% word_b = im2bw(word_a,max(0.1,thresh-0.05)); % ver 5

word_b = (word_a > I_t_r);

% Switch B/W for background / foreground

backW = 0;

[hh ww] = size(word_b);

if (word_b(1,1) == 1)

backW = backW + 1;

end

if (word_b(1,ww) == 1)

backW = backW + 1;

end

if (word_b(hh,1) == 1)

backW = backW + 1;

end

if (word_b(hh,ww) == 1)

backW = backW + 1;

end

if (backW <= 2)

word_b = ~word_b;

end

opSize = (h_high-h_low)/40; % size for further open/close operations

if (opSize >= 2)

word_c = imopen(~imopen(word_b,ones(opSize,opSize)),ones(opSize,opSize)); % Add in ver 5

else

word_c = ~word_b;

end

Y2(h_low:h_high,w_low:w_high) = word_c;

ketan=input('enter the text')

saveName = strcat('processedImages/',fileName,'_',num2str(i,'%#02i'),'_v5.jpg');

%imwrite(word_c,saveName,'jpg');

end

%clear Bounding3 Area3;

%%%%%%%%%%%%%%%%%%%%%

%Y4 = imclose(Y3, ones(15,15));

% Add in ver 5, can solve the Net-like obstacle problem, but not recommended for universal usage

%%%%%%%%%%%%%%%%%%%%%

%imwrite(Y3,strcat('processedImages/',fileName,'_',t_v4.jpg'));

%I_out = something;

%clear edge1 edge_m edge_m2 Y Y2;

% Print the time elapsed

fprintf('\nProcessing Time: %8.3f sec\n',toc);

fprintf('end of the program');

%end

================================================================

[uday modha]

sir below is my sample image link

http://www.flickr.com/photos/document1984/?saved=1

and here is the complete code

==========================================================

% EE368 Final Project Spring 2010-11

% Text Extraction Routine

% Author: Uday Modha

% Version: 7

% Dec 25,2011

% Situations that the code does not work well:

% - Text too large (average letter size > 0.3 image size)

% - Text too small (letter height < 15 px)

% - Largest text vs. smallest text ratio too high

% - Connected letters (not good for OCR either)

% - Low contrast with the background (unable to detect the edges of the

% letters

% - Noisy background (Too many noisy edges deteced)

% - prone to detect text located around the center of the image

%function I_out = textExt_1(I_in)

%clear all; clc; close all;

%for testI = 1:32

%t1 = cputime; % save cpu time

tic;

%fileName = strcat('test_a_',num2str(testI,'%#02i'));

%fileName = 'test_a_07';

%fileName = 'test6';

%loadName = strcat('rawImages/',fileName,'.jpg');

loadName='123.jpg';

I_in = imread(loadName);

% Input should be a 2D grayscale level image or 3D RGB image

dim_I = length(size(I_in));

if ((dim_I > 3) || (dim_I < 2))

fprintf('\nError! Input Input should be a 2D grayscale level image or 3D RGB image\n');

return;

end

% Initialization

%I_out = uint8(zeros(size(I_in))); % init output

% Calculate Intensity Image Y

% Y = 0.299R + 0.587G + 0.114B

if (dim_I == 3)

% if image is in RGB form

%Y = 0.299*double(I_in(:,:,1)) + 0.587*double(I_in(:,:,2)) + 0.114*double(I_in(:,:,3));

Y = 0.299*I_in(:,:,1)+0.587*I_in(:,:,2)+0.114*I_in(:,:,3);

%Y = rgb2gray(I_in);

uday=input('rgb');

else

% if image is grayscale

Y = I_in;

end

dsRate = 3;

% Downsampling

N = downsample(Y,dsRate);

N = N';

P = downsample(N,dsRate);

Y1 = P';

clear N P;

%edge0 = edge(Y1,'sobel',0.01);

%edge0 = edge(Y1,'prewitt');

%edge0 = edge(Y1,'log');

%edge0 = edge(Y1,'roberts');

%edge0 = edge(Y1,'canny');

% Increase contrast

Y1a = (double(Y1) / double(max(Y1(:)))).^1.5;

Y1a = uint8(Y1a / max(Y1a(:)) * 255);

%Y1 = uint8(double(Y1) / double(max(Y1(:))) * 255);

% Y is blurred using open-closed / closed-open filters

% (to reduced false edges and over-segmentation)

SE = strel('arbitrary', ones(3,3));

%Y_bl = (imclose(imopen(Y1,SE),SE) + imopen(imclose(Y1,SE),SE))/2;

Y_bl = (imclose(imopen(Y1a,SE),SE) + imopen(imclose(Y1a,SE),SE))/2;

% The Morphological gradient (MG) operator is applied to the blurred image

% Y_bl resulting in an image e_s

e_s = imdilate(Y_bl,SE) - imerode(Y_bl,SE);

% e_s is then thresholded to obtain a binary edge image

g1 = [-1 0 1]; g2 = [-1;0;1];

s = uint8(max(abs(conv2(double(e_s),double(g1),'same')),abs(conv2(double(e_s),double(g2),'same'))));

gamma = double(sum(sum(s.*e_s)))/double(sum(s(:)));

%fprintf('\ngamma = %8.3f\n',gamma);

edge0 = im2bw(e_s,gamma/double(max(e_s(:))));

%clear SE Y_bl e_s;

fprintf('\nProcessing Time: %8.3f sec\n',toc);

% A close operation before analyze the regions

edge1 = imclose(edge0,ones(3,3));

%edge1 = edge0;

%clear edge0;

% Regionprops operations

[L1 num1] = bwlabel(edge1,4);

R1 = regionprops(L1,'BoundingBox','Area');

%Major1 = regionprops(L1,'MajorAxisLength');

%Minor1 = regionprops(L1,'MinorAxisLength');

[pixNum_h ,pixNum_w] = size(edge1);

edge_m = zeros(pixNum_h, pixNum_w);

S = [];

fprintf('before loop %d',num1);

%num1=1;

for i = 1:num1

fprintf('entering into loop');

% box = R1(i).BoundingBox;

%area = R1(i).Area;

box = R1(i).BoundingBox;

area = R1(i).Area;

box_w = box(3);

box_h = box(4);

box_ratio = box_w / box_h;

area_ratio = area / box_w / box_h;

center_w = (box(1) + box(3)/2)/pixNum_w;

center_h = (box(2) + box(4)/2)/pixNum_h;

fprintf('\nwidth: %d\n',center_w);

fprintf('\nheight: %d\n',center_h);

%{

major = Major1(i).MajorAxisLength;

minor = Minor1(i).MinorAxisLength;

if (major / minor > 6)

continue;

end

%}

if ((center_w>0.15)&&(center_w<0.85)&&(center_h>0.2)&&(center_h<0.8)) % position filter

%if ((center_h>0.2)&&(center_h<0.8)) % position filter

if ((box_ratio<10)&&(box_w<0.5*pixNum_w)&&(box_w>10))

if ((box_h<0.3*pixNum_h)&&(box_h>15))

if (area_ratio>0.1)% geometry filter

%edge_m(L1==i) = 1;

edge_m(uint16(box(2):(box(2)+box(4))),uint16(box(1):(box(1)+box(3)))) = 1;

fprintf('\nnumber: %d\n',i);

S = [S ; box i]; % save bounding box information of the candidates

end

end

end

end

end

%clear Bounding1 Area1;

fprintf('hellow how r u?');

if size(S,1) < 1

break;

end

mean_h1 = mean(S(:,4)); % the mean height of all boxes

std_h1 = std(S(:,4)); % the standard deviation

for i = 1:size(S,1)

if (S(i,4) < mean_h1*0.6)||(S(i,4) > mean_h1*1.8)

% remove candidate from mask if the height is beyond mean +/- 2*std

edge_m(uint16(S(i,2):(S(i,2)+S(i,4))),uint16(S(i,1):(S(i,1)+S(i,3)))) = 0;

end

end

edge_m2 = imclose(edge_m, ones(1,1.5*uint16(mean_h1)));

% Analyze word candidates

[L3 ,num3] = bwlabel(edge_m2,4);

R3 = regionprops(L3,'BoundingBox','Area');

Y2 = false(size(Y));

sum_w3 = 0;

sum_h3 = 0;

for i = 1:num3

box = R3(i).BoundingBox;

sum_w3 = sum_w3 + box(3);

sum_h3 = sum_h3 + box(4);

end

mean_w3 = double(sum_w3) / num3;

mean_h3 = double(sum_h3) / num3;

for i = 1:num3

box = R3(i).BoundingBox;

area = R3(i).Area;

% if width is not like other texts in the image, skip

if (box(3) < mean_w3/3)

continue;

end

%{

% if height is not like other texts in the image, skip

if (box(4) < mean_h3/3)

continue;

end

%}

% if width / height is too small, not likely to be text, skip

if (box(3)/box(4) < 1)

continue;

end

% if area ratio is too small, not likely to be text, skip

if (area / box(3) /box(4) < 0.6)

continue;

end

h_low = dsRate*(max(uint16(box(2)),1)-1)+1;

h_high = dsRate*min(uint16(box(2)+box(4)),pixNum_h);

w_low = dsRate*(max(uint16(box(1)),1)-1)+1;

w_high = dsRate*min(uint16(box(1)+box(3)),pixNum_w);

word_a = Y(h_low:h_high,w_low:w_high);

thresh = graythresh(word_a);

% TRY TO ADD LOCAL THRESHOLD HERE!!!!!

% TRY TO ADD LOCAL THRESHOLD HERE!!!!!

varThresh = 30;

numInX = 5;

numInY = 1;

globalThresh = 255 * graythresh(word_a);

windowSizeX = floor(size(word_a,2)/numInX);

windowSizeY = floor(size(word_a,1)/numInY);

%I_t = uint8(size(word_a));

I_t = uint8(zeros(numInY,numInX));

for j = 1: numInX

if (j == numInX)

x1 = (numInX -1) * windowSizeX + 1;

x2 = size(word_a,2);

else

x1 = (j-1) * windowSizeX + 1;

x2 = j * windowSizeX;

end

for k = 1: numInY

if (k == numInY)

y1 = (k-1) * windowSizeY + 1;

y2 = k * windowSizeY;

else

y1 = (k-1) * windowSizeY + 1;

y2 = size(word_a,1);

end

localI = word_a(y1:y2,x1:x2);

% If variance greater than a certain value

% Do Otsu's method

if var(double(localI(:))) > varThresh

level = graythresh(localI(:));

%I_t(y1:y2,x1:x2) = 255 * level;

I_t(k,j) = 255 * level;

% Else, consider the region as uniform forground / background

else

%I_t(y1:y2,x1:x2) = globalThresh;

I_t(k,j) = 100;

end

end

end

I_t_r = imresize(I_t,size(word_a),'bilinear');

% word_b = im2bw(word_a,max(0.1,thresh-0.05)); % ver 5

word_b = (word_a > I_t_r);

% Switch B/W for background / foreground

backW = 0;

[hh ww] = size(word_b);

if (word_b(1,1) == 1)

backW = backW + 1;

end

if (word_b(1,ww) == 1)

backW = backW + 1;

end

if (word_b(hh,1) == 1)

backW = backW + 1;

end

if (word_b(hh,ww) == 1)

backW = backW + 1;

end

if (backW <= 2)

word_b = ~word_b;

end

opSize = (h_high-h_low)/40; % size for further open/close operations

if (opSize >= 2)

word_c = imopen(~imopen(word_b,ones(opSize,opSize)),ones(opSize,opSize)); % Add in ver 5

else

word_c = ~word_b;

end

Y2(h_low:h_high,w_low:w_high) = word_c;

ketan=input('enter the text')

saveName = strcat('processedImages/',fileName,'_',num2str(i,'%#02i'),'_v5.jpg');

%imwrite(word_c,saveName,'jpg');

end

%clear Bounding3 Area3;

%%%%%%%%%%%%%%%%%%%%%

%Y4 = imclose(Y3, ones(15,15));

% Add in ver 5, can solve the Net-like obstacle problem, but not recommended for universal usage

%%%%%%%%%%%%%%%%%%%%%

%imwrite(Y3,strcat('processedImages/',fileName,'_',t_v4.jpg'));

%I_out = something;

%clear edge1 edge_m edge_m2 Y Y2;

% Print the time elapsed

fprintf('\nProcessing Time: %8.3f sec\n',toc);

fprintf('end of the program');

%end

================================================================

[uday modha]

sir did you check the code??? what is my next step??? is there any new algorithms???

[Josephine]

Mr Uday, What is the text to be entered?

Answer 1

[Sean de Wolski]

John's Inpaint Nans

Answer 2

[Image Analyst]

I think roifill() would suit your needs. It's part of the Image Processing Toolbox. Or check this link: <http://spie.org/app/program/index.cfm?event_id=956859&export_id=x16280&ID=x16223&redir=x16223.xml&search_text=inpainting&type=2&programDays=0&x=29&y=10> for 8 talks this week at Electronic Imaging symposium on the subject.

Answer 3

[Josephine]

Is this code valid? does this give a promising result?

[Image Analyst]

Not sure what code you're talking about. But whichever it is, just try it with your images and see.

[Josephine]

The code above for removing text using inpainting. I tried using the image which was used in this code itself, but there is a point where it is asking to enter the text. So what should be the text.

[Josephine]

the code is

loadName='123.jpg'; I_in = imread(loadName);

% Input should be a 2D grayscale level image or 3D RGB image dim_I = length(size(I_in)); if ((dim_I > 3) (dim_I < 2)) fprintf('\nError! Input Input should be a 2D grayscale level image or 3D RGB image\n'); return; end

% Initialization %I_out = uint8(zeros(size(I_in))); % init output

% Calculate Intensity Image Y % Y = 0.299R + 0.587G + 0.114B if (dim_I == 3) % if image is in RGB form %Y = 0.299*double(I_in(:,:,1)) + 0.587*double(I_in(:,:,2)) + 0.114*double(I_in(:,:,3)); Y = 0.299*I_in(:,:,1)+0.587*I_in(:,:,2)+0.114*I_in(:,:,3);

%Y = rgb2gray(I_in); uday=input('rgb'); else % if image is grayscale Y = I_in; end

dsRate = 3; % Downsampling N = downsample(Y,dsRate); N = N'; P = downsample(N,dsRate); Y1 = P'; clear N P; %edge0 = edge(Y1,'sobel',0.01); %edge0 = edge(Y1,'prewitt'); %edge0 = edge(Y1,'log'); %edge0 = edge(Y1,'roberts'); %edge0 = edge(Y1,'canny');

% Increase contrast Y1a = (double(Y1) / double(max(Y1(:)))).^1.5; Y1a = uint8(Y1a / max(Y1a(:)) * 255);

%Y1 = uint8(double(Y1) / double(max(Y1(:))) * 255);

% Y is blurred using open-closed / closed-open filters % (to reduced false edges and over-segmentation) SE = strel('arbitrary', ones(3,3)); %Y_bl = (imclose(imopen(Y1,SE),SE) + imopen(imclose(Y1,SE),SE))/2; Y_bl = (imclose(imopen(Y1a,SE),SE) + imopen(imclose(Y1a,SE),SE))/2;

% The Morphological gradient (MG) operator is applied to the blurred image % Y_bl resulting in an image e_s e_s = imdilate(Y_bl,SE) - imerode(Y_bl,SE);

% e_s is then thresholded to obtain a binary edge image g1 = [-1 0 1]; g2 = [-1;0;1]; s = uint8(max(abs(conv2(double(e_s),double(g1),'same')),abs(conv2(double(e_s),double(g2),'same')))); gamma = double(sum(sum(s.*e_s)))/double(sum(s(:))); %fprintf('\ngamma = %8.3f\n',gamma); edge0 = im2bw(e_s,gamma/double(max(e_s(:))));

%clear SE Y_bl e_s;

fprintf('\nProcessing Time: %8.3f sec\n',toc);

% A close operation before analyze the regions edge1 = imclose(edge0,ones(3,3)); %edge1 = edge0; %clear edge0;

% Regionprops operations [L1 num1] = bwlabel(edge1,4); R1 = regionprops(L1,'BoundingBox','Area'); %Major1 = regionprops(L1,'MajorAxisLength'); %Minor1 = regionprops(L1,'MinorAxisLength');

[pixNum_h ,pixNum_w] = size(edge1); edge_m = zeros(pixNum_h, pixNum_w); S = []; fprintf('before loop %d',num1); %num1=1; for i = 1:num1 fprintf('entering into loop');

% box = R1(i).BoundingBox; %area = R1(i).Area; box = R1(i).BoundingBox;

area = R1(i).Area;

box_w = box(3); box_h = box(4); box_ratio = box_w / box_h; area_ratio = area / box_w / box_h; center_w = (box(1) + box(3)/2)/pixNum_w; center_h = (box(2) + box(4)/2)/pixNum_h;

fprintf('\nwidth: %d\n',center_w); fprintf('\nheight: %d\n',center_h); %{ major = Major1(i).MajorAxisLength; minor = Minor1(i).MinorAxisLength;

if (major / minor > 6) continue; end %} if ((center_w>0.15)&&(center_w<0.85)&&(center_h>0.2)&&(center_h<0.8)) % position filter %if ((center_h>0.2)&&(center_h<0.8)) % position filter if ((box_ratio<10)&&(box_w<0.5*pixNum_w)&&(box_w>10)) if ((box_h<0.3*pixNum_h)&&(box_h>15)) if (area_ratio>0.1)% geometry filter %edge_m(L1==i) = 1; edge_m(uint16(box(2):(box(2)+box(4))),uint16(box(1):(box(1)+box(3)))) = 1; fprintf('\nnumber: %d\n',i); % S = [S ; box i]; % save bounding box information of the candidates end end end end end S = [S ; box i]; % save bounding box information of the candidates %clear Bounding1 Area1; fprintf('hellow how r u?');

% if size(S,1) < 1 % % break; % end

mean_h1 = mean(S(:,4)); % the mean height of all boxes

std_h1 = std(S(:,4)); % the standard deviation for i = 1:size(S,1) if (S(i,4) < mean_h1*0.6)||(S(i,4) > mean_h1*1.8) % remove candidate from mask if the height is beyond mean +/- 2*std edge_m(uint16(S(i,2):(S(i,2)+S(i,4))),uint16(S(i,1):(S(i,1)+S(i,3)))) = 0; end end

edge_m2 = imclose(edge_m, ones(1,1.5*uint16(mean_h1)));

% Analyze word candidates [L3 ,num3] = bwlabel(edge_m2,4); R3 = regionprops(L3,'BoundingBox','Area');

Y2 = false(size(Y)); sum_w3 = 0; sum_h3 = 0; for i = 1:num3 box = R3(i).BoundingBox; sum_w3 = sum_w3 + box(3); sum_h3 = sum_h3 + box(4); end mean_w3 = double(sum_w3) / num3; mean_h3 = double(sum_h3) / num3;

for i = 1:num3 box = R3(i).BoundingBox; area = R3(i).Area;

% if width is not like other texts in the image, skip if (box(3) < mean_w3/3) continue; end %{ % if height is not like other texts in the image, skip if (box(4) < mean_h3/3) continue; end %} % if width / height is too small, not likely to be text, skip if (box(3)/box(4) < 1) continue; end % if area ratio is too small, not likely to be text, skip if (area / box(3) /box(4) < 0.6) continue; end

h_low = dsRate*(max(uint16(box(2)),1)-1)+1; h_high = dsRate*min(uint16(box(2)+box(4)),pixNum_h); w_low = dsRate*(max(uint16(box(1)),1)-1)+1; w_high = dsRate*min(uint16(box(1)+box(3)),pixNum_w);

word_a = Y(h_low:h_high,w_low:w_high);

thresh = graythresh(word_a);

% TRY TO ADD LOCAL THRESHOLD HERE!!!!! % TRY TO ADD LOCAL THRESHOLD HERE!!!!! varThresh = 30; numInX = 5; numInY = 1; globalThresh = 255 * graythresh(word_a); windowSizeX = floor(size(word_a,2)/numInX); windowSizeY = floor(size(word_a,1)/numInY); %I_t = uint8(size(word_a)); I_t = uint8(zeros(numInY,numInX)); for j = 1: numInX if (j == numInX) x1 = (numInX -1) * windowSizeX + 1; x2 = size(word_a,2); else x1 = (j-1) * windowSizeX + 1; x2 = j * windowSizeX; end

for k = 1: numInY if (k == numInY) y1 = (k-1) * windowSizeY + 1; y2 = k * windowSizeY; else y1 = (k-1) * windowSizeY + 1; y2 = size(word_a,1); end localI = word_a(y1:y2,x1:x2); % If variance greater than a certain value % Do Otsu's method if var(double(localI(:))) > varThresh level = graythresh(localI(:)); %I_t(y1:y2,x1:x2) = 255 * level; I_t(k,j) = 255 * level; % Else, consider the region as uniform forground / background else %I_t(y1:y2,x1:x2) = globalThresh; I_t(k,j) = 100; end end end

I_t_r = imresize(I_t,size(word_a),'bilinear'); % word_b = im2bw(word_a,max(0.1,thresh-0.05)); % ver 5 word_b = (word_a > I_t_r);

% Switch B/W for background / foreground backW = 0; [hh ww] = size(word_b); if (word_b(1,1) == 1) backW = backW + 1; end if (word_b(1,ww) == 1) backW = backW + 1; end if (word_b(hh,1) == 1) backW = backW + 1; end if (word_b(hh,ww) == 1) backW = backW + 1; end if (backW <= 2) word_b = ~word_b; end

opSize = (h_high-h_low)/40; % size for further open/close operations if (opSize >= 2) word_c = imopen(~imopen(word_b,ones(opSize,opSize)),ones(opSize,opSize)); % Add in ver 5 else word_c = ~word_b; end Y2(h_low:h_high,w_low:w_high) = word_c; ketan=input('enter the text') saveName = strcat('processedImages/',fileName,'_',num2str(i,'%#02i'),'_v5.jpg'); %imwrite(word_c,saveName,'jpg'); end

%clear Bounding3 Area3;

%%%%%%%%%%%%%%%%%%%%% %Y4 = imclose(Y3, ones(15,15)); % Add in ver 5, can solve the Net-like obstacle problem, but not recommended for universal usage %%%%%%%%%%%%%%%%%%%%%

%imwrite(Y3,strcat('processedImages/',fileName,'_',t_v4.jpg'));

%I_out = something; %clear edge1 edge_m edge_m2 Y Y2;

% Print the time elapsed fprintf('\nProcessing Time: %8.3f sec\n',toc); fprintf('end of the program');