Automatically Detect and Recognize Text in Natural ImagesOpen this ExampleThis example shows how to detect regions containing text in an image. It is a common task performed on unstructured scenes, for example when capturing video from a moving vehicle for the purpose of alerting a driver about a road sign. Segmenting out the text from a cluttered scene greatly helps with additional tasks such as optical character recognition O!"#.The automated text detection algorithm in this example starts with a large number of text region candidates and progressively removes those less likely to contain text. To highlight this algorithm$s flexibility, it is applied to images containing a road sign, a poster and a set of license plates.Step 1: Load image%oad the image. The text can be rotated in plane, but significant out of plane rotations may re&uire additional pre'processing.colorImage = imread('handicapSign.jpg');fgure; imshow(colorImage); title('Original image')Step 2: Detect MSER RegionsSince text characters usually have consistent color, we begin by finding regions of similar intensities in the image using the (SE" region detector )*+.% Detect and etract regionsgra!Image = rg"#gra!(colorImage);mser$egions = detect%S&$'eatures(gra!Image('$egion)rea$ange'(*+,- #---.);mser$egions/iels = 0ertcat(cell#mat(mser$egions./iel1ist));% etract regions% 2isuali3e the %S&$ regions o0erlaid on the original imagefgure; imshow(colorImage); hold on;plot(mser$egions( 'show/iel1ist'( true('show&llipses'(4alse);title('%S&$ regions');Some of these regions include extra background pixels. ,t this stage, the letter E and - in .TO/E-. combine into one region. ,lso notice that the space between bricks is included.Step 3: Use Canny Edge Detector to Further Segment the TetSince written text is typically placed on clear background, it tends to produce high response to edge detection. 0urthermore, an intersection of (SE" regions with the edges is going to produceregions that are even more likely to belong to text.% 5on0ert %S&$ piel lists to a "inar! mas6mser%as6 = 4alse(si3e(gra!Image));ind = su"#ind(si3e(mser%as6)( mser$egions/iels(7(#)( mser$egions/iels(7(+));mser%as6(ind) = true;% $un the edge detectoredge%as6 = edge(gra!Image( '5ann!');% 'ind intersection "etween edges and %S&$ regionsedge)nd%S&$Intersection = edge%as6 8 mser%as6;fgure; imshowpair(edge%as6( edge)nd%S&$Intersection( 'montage');title('5ann! edges and intersection o4 cann! edges with %S&$ regions')1ote that the original (SE" regions in mser%as6 still contain pixels that are not part of the text./e can use the edge mask together with edge gradients to eliminate those regions.2row the edges outward by using image gradients around edge locations. helper9row&dges helper function.*:( gDir. = imgradient(gra!Image);% ;ou must speci4! i4 the tet is light on dar6 "ac6ground or 0ice 0ersagradient9rown&dges%as6 = helper9row&dges(edge)nd%S&$Intersection( gDir( '1ightiA);% 5ompute normali3ed stro6e width 0ariation and compare to common 0aluei4 std(stro6ewidths)Gmean(stro6ewidths) ? -.H,a4terStro6eEidthiA) = -; % $emo0e 4rom tet candidatesendend% 2isuali3e the e=ect o4 stro6e width flteringfgure; imshowpair(region'iltered