getImageHere.m

function  I = getImageHere(img)
colorImage = img;
% colorImage = imread(img);
I = rgb2gray(img);
[mserRegions] = detectMSERFeatures(I, ...
    'RegionAreaRange',[200 8000],'ThresholdDelta',4);


%% Step 2: Remove Non-Text Regions Based On Basic Geometric Properties
% Although the MSER algorithm picks out most of the text, it also detects
% many other stable regions in the image that are not text. You can use a
% rule-based approach to remove non-text regions. For example, geometric
% properties of text can be used to filter out non-text regions using
% simple thresholds. Alternatively, you can use a machine learning approach
% to train a text vs. non-text classifier. Typically, a combination of the
% two approaches produces better results [4]. This example uses a simple
% rule-based approach to filter non-text regions based on geometric
% properties.
%
% There are several geometric properties that are good for discriminating
% between text and non-text regions [2,3], including:
%
% * Aspect ratio
% * Eccentricity
% * Euler number
% * Extent
% * Solidity
%
% Use |regionprops| to measure a few of these properties and then remove
% regions based on their property values.

% First, convert the x,y pixel location data within mserRegions into linear
% indices as required by regionprops.
sz = size(I);
pixelIdxList = cellfun(@(xy)sub2ind(sz, xy(:,2), xy(:,1)), ...
    mserRegions.PixelList, 'UniformOutput', false);

% Next, pack the data into a connected component struct.
mserConnComp.Connectivity = 8;
mserConnComp.ImageSize = sz;
mserConnComp.NumObjects = mserRegions.Count;
mserConnComp.PixelIdxList = pixelIdxList;

% 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) = [];


%% Step 3: Remove Non-Text Regions Based On Stroke Width Variation
% Another common metric used to discriminate between text and non-text is
% stroke width. _Stroke width_ is a measure of the width of the curves and
% lines that make up a character. Text regions tend to have little stroke
% width variation, whereas non-text regions tend to have larger variations.
%
% To help understand how the stroke width can be used to remove non-text
% regions, estimate the stroke width of one of the detected MSER regions.
% You can do this by using a distance transform and binary thinning
% operation [3].

% 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;


%
% In the images shown above, notice how the stroke width image has very
% little variation over most of the region. This indicates that the region
% is more likely to be a text region because the lines and curves that make
% up the region all have similar widths, which is a common characteristic
% of human readable text.
%
% In order to use stroke width variation to remove non-text regions using a
% threshold value, the variation over the entire region must be quantified
% into a single metric as follows:
%
% Compute the stroke width variation metric
strokeWidthValues = distanceImage(skeletonImage);
strokeWidthMetric = std(strokeWidthValues)/mean(strokeWidthValues);

%%
% Then, a threshold can be applied to remove the non-text regions. Note
% that this threshold value may require tuning for images with different
% font styles.

% Threshold the stroke width variation metric
strokeWidthThreshold = 0.4;
strokeWidthFilterIdx = strokeWidthMetric > strokeWidthThreshold;

%%
% The procedure shown above must be applied separately to each detected
% MSER region. The following for-loop processes all the regions, and then
% shows the results of removing the non-text regions using stroke width
% variation.

% 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) = [];


%% Step 4: Merge Text Regions For Final Detection Result
% At this point, all the detection results are composed of individual text
% characters. To use these results for recognition tasks, such as OCR, the
% individual text characters must be merged into words or text lines. This
% enables recognition of the actual words in an image, which carry more
% meaningful information than just the individual characters. For example,
% recognizing the string 'EXIT' vs. the set of individual characters
% {'X','E','T','I'}, where the meaning of the word is lost without the
% correct ordering.
%
% One approach for merging individual text regions into words or text lines
% is to first find neighboring text regions and then form a bounding box
% around these regions. To find neighboring regions, expand the bounding
% boxes computed earlier with |regionprops|. This makes the bounding boxes
% of neighboring text regions overlap such that text regions that are part
% of the same word or text line form a chain of overlapping bounding boxes.

% 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 = 0.03;
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);

%%
% Now, the overlapping bounding boxes can be merged together to form a
% single bounding box around individual words or text lines. To do this,
% compute the overlap ratio between all bounding box pairs. This quantifies
% the distance between all pairs of text regions so that it is possible to
% find groups of neighboring text regions by looking for non-zero overlap
% ratios. Once the pair-wise overlap ratios are computed, use a |graph| to
% find all the text regions "connected" by a non-zero overlap ratio.
%
% Use the |bboxOverlapRatio| function to compute the pair-wise overlap
% ratios for all the expanded bounding boxes, then use |graph| to find all
% the connected regions.

% 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);

%%
% The output of |conncomp| are indices to the connected text regions to
% which each bounding box belongs. Use these indices to merge multiple
% neighboring bounding boxes into a single bounding box by computing the
% minimum and maximum of the individual bounding boxes that make up each
% connected component.

% 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];

%%
% Finally, before showing the final detection results, suppress false text
% detections by removing bounding boxes made up of just one text region.
% This removes isolated regions that are unlikely to be actual text given
% that text is usually found in groups (words and sentences).

% Remove bounding boxes that only contain one text region
numRegionsInGroup = histcounts(componentIndices);
textBBoxes(numRegionsInGroup < 4, :) = [];

% [minval, minidx] = min(textBBoxes(:,1));
newarr = textBBoxes(:,3) .* textBBoxes(:,4);
newtext = [textBBoxes newarr]
if(size(newtext,1)>=2)
    
    newtext =  sortrows(newtext,-5);
    [Y X] = size(I);
    newtext(newtext(:,2)<Y/3,:) = [];
    if(size(newtext,1)>=2)
        %         [minval, minidx] = min(newtext(:,1));
        %     [row,col]=find(newtext==minval)
        % textBBoxes = newtext(row,[1 2 3 4]);
        % %textBBoxes = newtext(:,[1 2 3 4]);
        [minval, minidx] = max(newtext(:,5));
        [row,col] = find(newtext==minval);
        textBBoxes = newtext(row,[1 2 3 4]);
        %textBBoxes = newtext(:,[1 2 3 4]);
    else
        
        textBBoxes = newtext(:,[1 2 3 4]);
    end
else
    
    maxval = max(newarr);
    [row,col]=find(newarr==maxval)
    
    textBBoxes = textBBoxes(row,:);
end
% Show the final text detection result.
ITextRegion = insertShape(colorImage, 'Rectangle', textBBoxes,'LineWidth',3);
I2 = imcrop(colorImage,textBBoxes);
imshow(ITextRegion);
% figure
% imshow(ITextRegion)
% title('Detected Text')
%
% figure
% title('cropped image')
% imshow(I2)
imwrite(I2, 'NumberPlate.jpg');
I = I2;


end