豹纹癖This example shows how to detect regions in an image that contain text. This is a common task performed on unstructured scenes. Unstructured scenes are images that contain undetermined or random scenarios. For example, you can detect and recognize text automatically from captured video to alert a driver about a road sign. This is different than structured scenes, which contain known scenarios where the position of text is known beforehand.
Segmenting text from an unstructured scene greatly helps with additional tasks such as optical character recognition (OCR). The automated text detection algorithm in this example detects a large number of text region candidates and progressively removes those less likely to contain text.
? ? Step 1: Detect Candidate Text Regions Using MSER Step 2: Remove Non-Text Regions Based On Basic Geometric Properties ? Step 3: Remove Non-Text Regions Based On Stroke Width Variation ? ? ? Step 4: Merge Text Regions For Final Detection Result Step 5: Recognize Detected Text Using OCR References Step 1: Detect Candidate Text Regions Using MSER
The MSER feature detector works well for finding text regions [1]. It works well for text because the consistent color and high contrast of text leads to stable intensity profiles.
Use the detectMSERFeatures function to find all the regions within the image and plot these results. Notice that there are many non-text regions detected alongside the text.
colorImage = imread('handicapSign.jpg'); I = rgb2gray(colorImage);
% Detect MSER regions.
[mserRegions] = detectMSERFeatures(I, ...'RegionAreaRange',[200 8000],'ThresholdDelta',4);
figure imshow(I) hold on
plot(mserRegions, 'showPixelList', true,'showEllipses',false) title('MSER regions') hold off
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) = [];
% 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
