复杂背景下字符识别(ocr) - 图文 (2)

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;

% 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')

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) = [];

% 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

Step 4: Merge Text Regions For Final Detection Result