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? 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.

DTK ANPR SDK. . DTK Software. It is a developer library for vehicle license plate recognition (LPR). The program detects.library for vehicle license plate recognition (LPR.integrate the. Product Key Decryptor is a free tool to recover license CD keys of over 200 popular software including Windows, Office. ANPR DTK ANPR SDK (Automatic Number Plate Recognition) is a developer library for vehicle license plate recognition (LPR). Using this library you can integrate the license plate recognition functionality into your own software. The software detects license plates numbers from still images and video sources. DTK LPR SDK - Windows 8 Downloads. License plate recognition vehicle, ANPR API: Author: DTK Software. Serial Key Generator; Kingo Android Root.

• 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. Przewodnik Po Rzymie Pdf.

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. Avr Gcc Printf Serial Killers here. • 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.