This MATLAB function returns an ocrText object containing optical character recognition information from the input image, I. Browse and Read Ocr Matlab Code For Pdf Ocr Matlab Code For Pdf Read more and get great! That's what the book enPDFd ocr matlab code for pdf will give for every. MATLAB Examples; Object Detection and. (OCR) Automatically Detect and Recognize Text in Natural. This example code is a good starting point for developing more.

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 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. % 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] 0.9; filterIdx = filterIdx [mserStats.EulerNumber].

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.

Turbo C 16 Bit. 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'). % 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'). 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.