MATLAB: OCR is not working properly with custom traffic sign

Computer Vision Toolboxmserocr

Hello. I am a student who is studying computer vision through an example of MATLAB.
I tested it by inserting a different picture than used by the example.
However, the I character was not recognized, and the O was misinterpreted as a G. May I ask why?
Below is the code and the picture that I tested.
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colorImage = imread('handicapSign.png');
I = rgb2gray(colorImage);
% Detect MSER regions.
[mserRegions, mserConnComp] = detectMSERFeatures(I, ...
    'RegionAreaRange',[200 8000],'ThresholdDelta',4);
figure
imshow(I)
hold on
plot(mserRegions, 'showPixelList', true,'showEllipses',false)
title('MSER regions')
hold off
% Use regionprops to measure MSER properties
mserStats = regionprops(mserConnComp, 'BoundingBox', 'Eccentricity', ...
    'Solidity', 'Extent', 'Euler', 'Image');
% Compute the aspect ratio using bounding box data.
bbox = vertcat(mserStats.BoundingBox);
w = bbox(:,3);
h = bbox(:,4);
aspectRatio = w./h;
% Threshold the data to determine which regions to remove. These thresholds
% may need to be tuned for other images.
filterIdx = aspectRatio' > 3;
filterIdx = filterIdx | [mserStats.Eccentricity] > .995 ;
filterIdx = filterIdx | [mserStats.Solidity] < .3;
filterIdx = filterIdx | [mserStats.Extent] < 0.2 | [mserStats.Extent] > 0.9;
filterIdx = filterIdx | [mserStats.EulerNumber] < -4;
% Remove regions
mserStats(filterIdx) = [];
mserRegions(filterIdx) = [];
% Show remaining regions

figure
imshow(I)
hold on
plot(mserRegions, 'showPixelList', true,'showEllipses',false)
title('After Removing Non-Text Regions Based On Geometric Properties')
hold off
% Get a binary image of the a region, and pad it to avoid boundary effects
% during the stroke width computation.
regionImage = mserStats(6).Image;
regionImage = padarray(regionImage, [1 1]);
% Compute the stroke width image.
distanceImage = bwdist(~regionImage);
skeletonImage = bwmorph(regionImage, 'thin', inf);
strokeWidthImage = distanceImage;
strokeWidthImage(~skeletonImage) = 0;
% Show the region image alongside the stroke width image.
figure
subplot(1,2,1)
imagesc(regionImage)
title('Region Image')
subplot(1,2,2)
imagesc(strokeWidthImage)
title('Stroke Width Image')
% Compute the stroke width variation metric
strokeWidthValues = distanceImage(skeletonImage);  
strokeWidthMetric = std(strokeWidthValues)/mean(strokeWidthValues);
% Threshold the stroke width variation metric
strokeWidthThreshold = 0.4;
strokeWidthFilterIdx = strokeWidthMetric > strokeWidthThreshold;
% Process the remaining regions
for j = 1:numel(mserStats)
   
    regionImage = mserStats(j).Image;
    regionImage = padarray(regionImage, [1 1], 0);
   
    distanceImage = bwdist(~regionImage);
    skeletonImage = bwmorph(regionImage, 'thin', inf);
   
    strokeWidthValues = distanceImage(skeletonImage);
   
    strokeWidthMetric = std(strokeWidthValues)/mean(strokeWidthValues);
   
    strokeWidthFilterIdx(j) = strokeWidthMetric > strokeWidthThreshold;
   
end
% Remove regions based on the stroke width variation
mserRegions(strokeWidthFilterIdx) = [];
mserStats(strokeWidthFilterIdx) = [];
% Show remaining regions
figure
imshow(I)
hold on
plot(mserRegions, 'showPixelList', true,'showEllipses',false)
title('After Removing Non-Text Regions Based On Stroke Width Variation')
hold off
% Get bounding boxes for all the regions
bboxes = vertcat(mserStats.BoundingBox);
% Convert from the [x y width height] bounding box format to the [xmin ymin
% xmax ymax] format for convenience.
xmin = bboxes(:,1);
ymin = bboxes(:,2);
xmax = xmin + bboxes(:,3) - 1;
ymax = ymin + bboxes(:,4) - 1;
% Expand the bounding boxes by a small amount.
expansionAmount = 0.02;
xmin = (1-expansionAmount) * xmin;
ymin = (1-expansionAmount) * ymin;
xmax = (1+expansionAmount) * xmax;
ymax = (1+expansionAmount) * ymax;
% Clip the bounding boxes to be within the image bounds
xmin = max(xmin, 1);
ymin = max(ymin, 1);
xmax = min(xmax, size(I,2));
ymax = min(ymax, size(I,1));
% Show the expanded bounding boxes
expandedBBoxes = [xmin ymin xmax-xmin+1 ymax-ymin+1];
IExpandedBBoxes = insertShape(colorImage,'Rectangle',expandedBBoxes,'LineWidth',3);
figure
imshow(IExpandedBBoxes)
title('Expanded Bounding Boxes Text')
% Compute the overlap ratio
overlapRatio = bboxOverlapRatio(expandedBBoxes, expandedBBoxes);
% Set the overlap ratio between a bounding box and itself to zero to
% simplify the graph representation.
n = size(overlapRatio,1);
overlapRatio(1:n+1:n^2) = 0;
% Create the graph
g = graph(overlapRatio);
% Find the connected text regions within the graph
componentIndices = conncomp(g);
% Merge the boxes based on the minimum and maximum dimensions.
xmin = accumarray(componentIndices', xmin, [], @min);
ymin = accumarray(componentIndices', ymin, [], @min);
xmax = accumarray(componentIndices', xmax, [], @max);
ymax = accumarray(componentIndices', ymax, [], @max);
% Compose the merged bounding boxes using the [x y width height] format.
textBBoxes = [xmin ymin xmax-xmin+1 ymax-ymin+1];
% Remove bounding boxes that only contain one text region
numRegionsInGroup = histcounts(componentIndices);
textBBoxes(numRegionsInGroup == 1, :) = [];
% Show the final text detection result.
ITextRegion = insertShape(colorImage, 'Rectangle', textBBoxes,'LineWidth',3);
figure
imshow(ITextRegion)
title('Detected Text')
ocrtxt = ocr(I, textBBoxes);
[ocrtxt.Text]

Best Answer

You can see that the I was detected in figure 1, then filtered out by the time it got to figure 2. Look at the comment:
% Threshold the data to determine which regions to remove. These thresholds
% may need to be tuned for other images.
You need to adjust them. Perhaps the aspect ratio of the I is more than 3 - it probably is.
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