Can anyone please clear my doubt on this. I'm doing character recognition from vehicle number plates.

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Can anyone please clear my doubt on this. I'm doing character recognition from vehicle number plates.
My requirement is to isolate the number plate region from the whole image and i hv to recognize the characters in that..
1. How to separate the region of number plate alone from the whole car.? 2. How to carry the recognition phase?

Answers (1)

Rinku
Rinku on 31 Dec 2014
Edited: Walter Roberson on 27 Jun 2017
clc; % Clear command window.
clear all; % Delete all variables.
close all; % Close all figure windows except those created by imtool.
imtool close all; % Close all figure windows created by imtool.
workspace; % Make sure the workspace panel is showing.
% Read Image
I = imread ('car3.jpg');
figure(1);
imshow(I);title('Original Image')
% Extract Y component (Convert an Image to Gray)
Igray = rgb2gray(I);
[rows cols] = size(Igray);
%%Dilate and Erode Image in order to remove noise
Idilate = Igray;
for i = 1:rows
for j = 2:cols-1
temp = max(Igray(i,j-1), Igray(i,j));
Idilate(i,j) = max(temp, Igray(i,j+1));
end
end
I = Idilate;
figure(2);
imshow(Igray);title('RGB to Gray Conversion Image')
figure(3);
imshow(Idilate);title('Dilated Image')
figure(4);
imshow(I);title('After Removing Noise')
difference = 0;
sum = 0;
total_sum = 0;
difference = uint32(difference);
%%PROCESS EDGES IN HORIZONTAL DIRECTION
disp('Processing Edges Horizontally...');
max_horz = 0;
maximum = 0;
for i = 2:cols
sum = 0;
for j = 2:rows
if(I(j, i) > I(j-1, i))
difference = uint32(I(j, i) - I(j-1, i));
else
difference = uint32(I(j-1, i) - I(j, i));
end
if(difference > 20)
sum = sum + difference;
end
end
horz1(i) = sum;
% Find Peak Value
if(sum > maximum)
max_horz = i;
maximum = sum;
end
total_sum = total_sum + sum;
end
average = total_sum / cols;
figure(5);
% Plot the Histogram for analysis
subplot(3,1,1);
plot (horz1);title('Horizontal Edge Processing Histogram');
xlabel('Column Number ->');
ylabel('Difference ->');
%%Smoothen the Horizontal Histogram by applying Low Pass Filter
disp('Passing Horizontal Histogram through Low Pass Filter...');
sum = 0;
horz = horz1;
for i = 21:(cols-21)
sum = 0;
for j = (i-20):(i+20)
sum = sum + horz1(j);
end
horz(i) = sum / 41;
end
subplot(3,1,2);
plot (horz);title('Histogram after passing through Low Pass Filter');
xlabel('Column Number ->');
ylabel('Difference ->');
%%Filter out Horizontal Histogram Values by applying Dynamic Threshold
disp('Filter out Horizontal Histogram...');
for i = 1:cols
if(horz(i) < average)
horz(i) = 0;
for j = 1:rows
I(j, i) = 0;
end
end
end
subplot(3,1,3);
plot (horz);title('Histogram after Filtering');
xlabel('Column Number ->');
ylabel('Difference ->');
%%PROCESS EDGES IN VERTICAL DIRECTION
difference = 0;
total_sum = 0;
difference = uint32(difference);
disp('Processing Edges Vertically...');
maximum = 0;
max_vert = 0;
for i = 2:rows
sum = 0;
for j = 2:cols %cols
if(I(i, j) > I(i, j-1))
difference = uint32(I(i, j) - I(i, j-1));
end
if(I(i, j) <= I(i, j-1))
difference = uint32(I(i, j-1) - I(i, j));
end
if(difference > 20)
sum = sum + difference;
end
end
vert1(i) = sum;
%%Find Peak in Vertical Histogram
if(sum > maximum)
max_vert = i;
maximum = sum;
end
total_sum = total_sum + sum;
end
average = total_sum / rows;
figure(6)
subplot(3,1,1);
plot (vert1);title('Vertical Edge Processing Histogram');
xlabel('Row Number ->');
ylabel('Difference ->');
%%Smoothen the Vertical Histogram by applying Low Pass Filter
disp('Passing Vertical Histogram through Low Pass Filter...');
sum = 0;
vert = vert1;
for i = 21:(rows-21)
sum = 0;
for j = (i-20):(i+20)
sum = sum + vert1(j);
end
vert(i) = sum / 41;
end
subplot(3,1,2);
plot (vert);title('Histogram after passing through Low Pass Filter');
xlabel('Row Number ->');
ylabel('Difference ->');
%%Filter out Vertical Histogram Values by applying Dynamic Threshold
disp('Filter out Vertical Histogram...');
for i = 1:rows
if(vert(i) < average)
vert(i) = 0;
for j = 1:cols
I(i, j) = 0;
end
end
end
subplot(3,1,3);
plot (vert);title('Histogram after Filtering');
xlabel('Row Number ->');
ylabel('Difference ->');
figure(7), imshow(I);
%%Find Probable candidates for Number Plate
j = 1;
for i = 2:cols-2
if(horz(i) ~= 0 && horz(i-1) == 0 && horz(i+1) == 0)
column(j) = i;
column(j+1) = i;
j = j + 2;
elseif((horz(i) ~= 0 && horz(i-1) == 0) || (horz(i) ~= 0 && horz(i+1) == 0))
column(j) = i;
j = j+1;
end
end
j = 1;
for i = 2:rows-2
if(vert(i) ~= 0 && vert(i-1) == 0 && vert(i+1) == 0)
row(j) = i;
row(j+1) = i;
j = j + 2;
elseif((vert(i) ~= 0 && vert(i-1) == 0) || (vert(i) ~= 0 && vert(i+1) == 0))
row(j) = i;
j = j+1;
end
end
[temp column_size] = size (column);
if(mod(column_size, 2))
column(column_size+1) = cols;
end
[temp row_size] = size (row);
if(mod(row_size, 2))
row(row_size+1) = rows;
end
%%Region of Interest Extraction
%Check each probable candidate
for i = 1:2:row_size
for j = 1:2:column_size
% If it is not the most probable region remove it from image
if(~((max_horz >= column(j) && max_horz <= column(j+1)) && (max_vert >= row(i) && max_vert <= row(i+1))))
%This loop is only for displaying proper output to User
for m = row(i):row(i+1)
for n = column(j):column(j+1)
I(m, n) = 0;
end
end
end
end
end
figure(8), imshow(I);title('Number Plate Region')
II=im2bw(I);
s=regionprops(II,'Area','BoundingBox');
[~,ii] = sort([s.Area],'descend');
out = imcrop(I,s(ii(1)).BoundingBox);
figure(9), imshow(out);title('Identity Number Plate')
you can try with this code

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