Step 4: Now we can start looking for contours on our image contours=cv2.findContours(py(),cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours = ab_contours(contours) contours = sorted(contours,key=cv2.contourArea, reverse = True) screenCnt = None The test image that I am using for this tutorial is shown below. If you want to jump straight into the code without explanation then you can scroll down to the bottom of this page, where the complete code is provided or click here for GitHub link. We will then use the same image for Character Segmentation and Character Recognition as well.
Let’s take a sample image of a car and start with detecting the License Plate on that car.
So, we can perform OCR (Optical Character Recognition) on it to detect the number 1. Character Recognition: Now, the new image that we obtained in the previous step is sure to have some characters (Numbers/Alphabets) written on it. Again this can be done easily using OpenCV.ģ. Character Segmentation: Once we have detected the License Plate we have to crop it out and save it as a new image. This gets trickier if the image does not even have a car, in this case we will an additional step to detect the car and then the license plate.Ģ. Normally the detection algorithm is trained based on the position of camera and type of number plate used in that particular country. The accuracy can be improved if we know the exact size, color and approximate location of the number plate. We will use the contour option in OpenCV to detect for rectangular objects to find the number plate. License Plate Detection: The first step is to detect the License plate from the car.
Steps involved in License Plate Recognitionġ. This technology is used in various security and traffic applications. License Plate Recognition is an image-processing technology used to identify vehicles by their license plates. License Plate Recognition using OpenCV Python
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