vowel.m

一个字符识别的matlab程序

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   %%                                                            %%
 %%       Prof. Sclaroff's CS585 Image avd Video Processing       %%
%%                         Project  ONE                            %%
%%           C H A R A C T E R   R E C O G N I T I O N             %%
%%                                                                 %%
%%                    by Stanislav Rost                    %%
 %%                       ID:  31764117                           %%
  %%                                                             %%
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function result = vowel(filename)
% VOWEL.M
%
% function result = vowel(filename)
%
% Arguments:  name of the file containing scanned letters
%
% Vowel.m performs vowel recognition using moments, part ratios and
% compactness.
%
% As a result, the function shows an image with letters O marked
% blue, E magenta, A red, U green.  All other letters are white/gray.
%
% Assumptions:  
%  - if the paragraph is tilted, then it is rectangular 
%  - no more than two letters are joined at any instance of joined letters


imageData = im2bw(imread(filename,'tif'),0.5);
imageData = ~imageData;

% Find out average height of letters
avgLabel = bwlabel(imageData, 4);
avgLetters = imfeature(avgLabel, 'BoundingBox');
numAvg = max(avgLabel(:));

avgHeight = 0;
for i = 1:numAvg,
	avgHeight = avgHeight + avgLetters(i).BoundingBox(4);
end
avgHeight = round(avgHeight / numAvg);

clear avgLetters



% #################### ROTATOR  ############################
% Rotate the image so that letters are properly oriented

% This is how it's done:  we use the find out imfeature's Orientation
% for the entire paragraph (so the assumption here is that the paragraph
% is wider than it is tall), and then rotate by that value...

% only if the image is tilted, which can be checked by breaking the image in 2,
% and then seeing if the top boundaries on their bounding boxes are about the
% same height

origFeature = imfeature(double(imageData), 'BoundingBox', 'Orientation');
angle = - origFeature.Orientation;

midpoint = floor(origFeature.BoundingBox(3)/2);
leftHalf = imageData(:,1:midpoint);
rightHalf = imageData(:,(midpoint+1):end);

leftFeature = imfeature(double(leftHalf), 'BoundingBox');
rightFeature = imfeature(double(rightHalf), 'BoundingBox');

% If the distance between upper boundaries of bounding boxes
% is bigger than half the average letter size, rotate image.
if (abs(leftFeature.BoundingBox(2) - rightFeature.BoundingBox(2)) > avgHeight/2 )
	disp('Rotation accepted');
	imageData = imrotate(imageData, angle, 'nearest');
end


% ################### LABEL 1 ##############################
imageData = bwlabel(imageData, 4);

% #################### SPLITTER ############################

% Split the letters that need to be split

% First, calculate the averge width of letters
splitLetters = imfeature(imageData, 'BoundingBox', 'Image');
numSplit = max(imageData(:));

avgWidth = 0;
for i = 1:numSplit,
	avgWidth = avgWidth + splitLetters(i).BoundingBox(3);
end
avgWidth = round(avgWidth / numSplit);

% Now, go through all the letters and if their width
% is bigger than 1.5 average then split them.
for i = 1:numSplit,
	if splitLetters(i).BoundingBox(3) > 1.5 * avgWidth

		[ rows columns ] = size(splitLetters(i).Image);

		% Draw vertical line
		splitLetters(i).Image(:, avgWidth ) = 0;
		splitLetters(i).Image(:, splitLetters(i).BoundingBox(3) - avgWidth) = 0;

		if (columns>avgWidth*2.5)
			splitLetters(i).Image(:, columns - avgWidth +2 ) = 0;
		end

		for k = 1:2,
			splitLetters(i).Image =erode(splitLetters(i).Image, ...
				[ 0,1,0;0,1,0 ]);
			splitLetters(i).Image = bwmorph(splitLetters(i).Image, 'spur');
		end

		% insert the split image back into the original image
		xoffset = round(splitLetters(i).BoundingBox(1));
		yoffset = round(splitLetters(i).BoundingBox(2));

		% Put the image back into training data
		for c = 1:splitLetters(i).BoundingBox(3),
			for d = 1:splitLetters(i).BoundingBox(4),
				imageData(yoffset+d-1, xoffset+c-1) = ...
					splitLetters(i).Image(d,c);
			end
		end

	end
end

imageData = im2bw(imageData, 0.5);
imageData = bwlabel(imageData, 4);

% #################### RECOGNITION LOOP ################

% Relabel everything because it might be rotated
imageLetters = imfeature(imageData,'Image', 'Area', 'EulerNumber', 'BoundingBox');
imageNumLetters = max(imageData(:));

% Load recognizable letter data from file(s)

letterName(1) = 'o';
letterName(2) = 'e';
letterName(3) = 'a';
letterName(4) = 'u';

load 'o.mat' meanO covO

letterObj(1) = struct('Name', 'o', ...
		'Mean', meanO, ...
		'Covariance', covO);

load 'e.mat' meanE covE

letterObj(2) = struct('Name', 'e', ...
		'Mean', meanE, ...
		'Covariance', covE);

load 'a.mat' meanA covA

letterObj(3) = struct('Name', 'a', ...
		'Mean', meanA, ...
		'Covariance', covA);

load 'u.mat' meanU covU

letterObj(4) = struct('Name', 'u', ...
		'Mean', meanU, ...
		'Covariance', covU);

% Save a copy of a colormap
cmap = colormap;

% A spinning tick mark indicates that processing is underway
tickArray = [ '-' '/' '|' '\' '-' '/' '|' '\' ];

% Main recognition loop

% newColors will hold the number of colors that was added as a result of splitting
newColors = 0;

% Main recognition loop - go through each letter (or joined sequence of letters)
for k = 1:imageNumLetters,

	% Output the tick mark
	temp = mod(k,8);

	if temp == 0
		temp = 8;
	end

	disp(tickArray(temp));

		% It's a single letter   

		% Compare each letter to be recognized to each letter
		matchLetter = letter_compare(letterObj, imageLetters(k));

		% Highlight the letter
		switch matchLetter
			case 1, palentry = [ 0.0 0.0 1.0 ];
			case 2, palentry = [ 1.0 0.0 1.0 ];
			case 3, palentry = [ 1.0 0.0 0.0 ];
			case 4, palentry = [ 0.0 1.0 0.0 ];
			otherwise, palentry = [ 0 0 0 ];
		end

	        cmap(k,:) = palentry;


end

% End main recognition loop

% Shift the data values up to make room for black background
imageData = imageData +1;
% Add the black color into the palette
tempMap = [ 1,1,1; cmap ];

% Show results
figure;
image(imageData);
colormap(tempMap);