Optical character recognition for seven-segment display digit.[MATLAB]

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Esther 2012-9-8

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https://ww2.mathworks.cn/matlabcentral/answers/47668-optical-character-recognition-for-seven-segment-display-digit-matlab

评论： Brett Shoelson 2021-3-3

Hi. recently i am doing on a project of Optical character recognition for seven-segment display digit. I am asked to crop out [attach photo] the A,B,C,D,E,F,G accordingly using AUTO -CROPPING.

http://i1149.photobucket.com/albums/o582/Pillowgerr/24bd59a8.jpg

and i had a hard time figuring the codes & ways succesfully do it. I hope any of you can assist me with coding and explanations. thank you so much! :)

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Manpreet Singh 2017-2-27

m5.JPG

sir how to recognize seven segment characters in this image by ocr plz help

Image Analyst 2017-2-27

I gave code for this below. Adapt it as needed.

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采纳的回答

Image Analyst 2012-9-8

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https://ww2.mathworks.cn/matlabcentral/answers/47668-optical-character-recognition-for-seven-segment-display-digit-matlab#answer_58262

编辑：Image Analyst 2012-9-8

在 MATLAB Online 中打开

You don't need to crop, unless you want to, or unless your segments wander all over from image to image. You segments should be in the same place in each image unless for some reason your camera is not mounted properly, or your display is bouncing around.

To check whether segments are on or off is pretty easy. Just look at the color channels to extract the segments, and look in boxes located at certain locations (the middle of where each segment is expected to be) for whether the pixels there are white or black. See my demo to get you started:

% Demo to check a 7 segment display.
% By ImageAnalyst
clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
imtool close all;  % Close all imtool figures.
clear;  % Erase all existing variables.
workspace;  % Make sure the workspace panel is showing.
fontSize = 14;
% Read in a standard MATLAB gray scale demo image.
folder = 'C:\Users\Esther\Documents\Temporary';
baseFileName = '7segmentdisplay.jpg';
% Get the full filename, with path prepended.
fullFileName = fullfile(folder, baseFileName);
if ~exist(fullFileName, 'file')
  % Didn't find it there.  Check the search path for it.
  fullFileName = baseFileName; % No path this time.
  if ~exist(fullFileName, 'file')
    % Still didn't find it.  Alert user.
    errorMessage = sprintf('Error: %s does not exist.', fullFileName);
    uiwait(warndlg(errorMessage));
    return;
  end
end
rgbImage = imread(fullFileName);
% Get the dimensions of the image.  numberOfColorBands should be = 3.
[rows columns numberOfColorBands] = size(rgbImage);
% Display the original color image.
subplot(2, 2, 1);
imshow(rgbImage, []);
title('Original Color Image', 'FontSize', fontSize);
% Enlarge figure to full screen.
set(gcf, 'units','normalized','outerposition',[0 0 1 1]);
% Extract the individual red, green, and blue color channels.
redChannel = rgbImage(:, :, 1);
greenChannel = rgbImage(:, :, 2);
blueChannel = rgbImage(:, :, 3);
% Display the original color image.
subplot(2, 2, 2);
imshow(redChannel, []);
title('Red Channel', 'FontSize', fontSize);
subplot(2, 2, 3);
imshow(greenChannel, []);
title('Green Channel', 'FontSize', fontSize);
subplot(2, 2, 4);
imshow(blueChannel, []);
title('Blue Channel', 'FontSize', fontSize);
% Extract just the segments and not the numbers.
figure;
binaryImage = ~(redChannel < 200) & (blueChannel < 200);
% subplot(2, 2, 1);
imshow(binaryImage, []);
axis on;
drawnow;
hold on;
% Enlarge figure to full screen.
set(gcf, 'units','normalized','outerposition',[0 0 1 1]);
% Now define locations to check.
row1 = 35;
row2 = 110;
row3 = 175;
row4 = 260;
row5 = 330;
col1 = 70;
col2 = 175;
col3 = 275;
% Plot boxes around there to check.
title('Checking Image Inside Red Boxes', 'FontSize', fontSize);
boxWidth = 30;
% First check top segment.
row = row1;
col = col2;
boxX = [col col+boxWidth col+boxWidth col col];
boxY = [row row row + boxWidth, row + boxWidth, row];
plot(boxX, boxY, 'ro-');
imageInsideBox = binaryImage(row:row + boxWidth, col:col+boxWidth)
% Determine if there are any pixels set inside that box.
segmentState(1) = any(imageInsideBox(:))
% Now check upper left segment.
row = row2;
col = col1;
boxX = [col col+boxWidth col+boxWidth col col];
boxY = [row row row + boxWidth, row + boxWidth, row];
plot(boxX, boxY, 'ro-');
imageInsideBox = binaryImage(row:row + boxWidth, col:col+boxWidth)
% Determine if there are any pixels set inside that box.
segmentState(2) = any(imageInsideBox(:))
% Now check lower right segment.
row = row4;
col = col3;
boxX = [col col+boxWidth col+boxWidth col col];
boxY = [row row row + boxWidth, row + boxWidth, row];
plot(boxX, boxY, 'ro-');
imageInsideBox = binaryImage(row:row + boxWidth, col:col+boxWidth)
% Determine if there are any pixels set inside that box.
segmentState(6) = any(imageInsideBox(:))

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Image Analyst 2016-1-25

在 MATLAB Online 中打开

You don't need to do that. You can just have 7 skinny rectangles and get the mean gray level in each. If it's less than 128, there is a black stick in it. If it's more than that, it's all white and the segment is not turned on. Then you just have a look up table of 128 elements that gives you the number based on what elements are on. For example, if a "1" is elements "f" and "e" on, and all others off, and if the bits are abcdefg, then the pattern for a 1 would be 0000110. Well, that's a 6, so in your look up table, you'd have a 1 at element 6. The other 8 digits would be located at 8 other numbers because they have different patterns. As another example, 5 would have segments afgcd on, so the index would be 1011011, which is 91, so if you get 91, that means the pattern is a 5.

lookUpTable = zeros(128, 1);
lookUpTable(6) = 1;
lookUpTable(91) = 5;
% And so on for the other 8 digits.

So just read the intensities in the segments, convert to a binary number then a decimal number for the index and get the number.

theNumber = lookUpTable(index);

Brett Shoelson 2021-3-3

I would suggest that instead of trying to do this with image morphology or segment detection, you install a 7-segment font (freely available--Google), and train the font using the OCR Training App. Probably easier, almost certainly more accurate.

Cheers,

Brett

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