License Plate Recognition Source Code C Language

I have a web site that allows users to upload images of cars and I would like to put a privacy filter in place to detect registration plates on the vehicle and blur them. The blurring is not a problem but is there a library or component (open source preferred) that will help with finding a licence within a photo?

Search license plate recognition python, 300 result(s) found Vehicle license plate recognition A complete license plate recognition system should include vehicle detection, image acquisition, image processing, vehicle license plate location segmentation, character recognition, character, etc. Jan 09, 2016  Dangerous Biggest Heavy Duty Hammer Forging Process, Fastest Hydraulic Steel Forging Machine - Duration: 16:17. LA Machines Recommended for you. Shabloni voennoj formi dlya foto na dokumenti ukraina.

Caveats; • I know nothing is perfect and image recognition of this type will provide false positive and negatives. • I appreciate that we could ask the user to select the area to blur and we will do this as well, but the question is specifically about finding that data programmatically; so answers such as 'get a person to check every image' is not helpful. • This software method is called 'Automatic Number Plate Recognition' in the UK but I cannot see any implementations of it as libraries. • Any language is great although.Net is preferred. EDIT: I wrote a for this. As your objective is blurring (for privacy protection), you basically need a high detector as a first step.

Here's how to go about doing this. The included code hints use OpenCV with Python. • Convert to Grayscale.

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• Apply Gaussian Blur. Img = cv2.imread('input.jpg',1) img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) img_gray = cv2.GaussianBlur(img_gray, (5,5), 0) Let the input image be the following. • Apply Sobel Filter to detect vertical edges. • Threshold the resultant image using strict threshold or OTSU's binarization.

Cv2.Sobel(image, -1, 1, 0) cv2.threshold() • Apply a Morphological Closing operation using suitable structuring element. (I used 16x4 as structuring element) se = cv2.getStructuringElement(cv2.MORPH_RECT,(16,4)) cv2.morphologyEx(image, cv2.MORPH_CLOSE, se) Resultant Image after Step 5. • Find external contours of this image. Cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) • For each contour, find the minAreaRect() bounding it. • Select rectangles based on aspect ratio, minimum and maximum area, and angle with the horizontal.