In this blog, we will learn how to detect a number plate of a car and extract its values using computer vision. Today, we’re going to build one such project to detect number plates using computer vision which helps in e-challan, and security monitoring. They are using deep learning and computer vision to detect the number plate in the car and extract the license number from that.
With the increase in road fraud, the cops are also becoming smarter.
In this fastest era of technology, it is very difficult to stop every vehicle on the road and check its number plate in the search for one criminal car. Image = cv2.putText(image, text, (x-100, y-50), cv2.FONT_HERSHEY_SIMPLEX, 3, (0, 255, 0), 6, cv2.LINE_AA)Ĭv2.This article was published as a part of the Data Science Blogathon Text = pytesseract.image_to_string(license_plate, lang='eng') # Find Perimeter of contour and it should be a closed contourĪpprox = cv2.approxPolyDP(contour, 0.02 * perimeter, True) # Find the contour with 4 potential corners and create a Region of Interest around it # Initialize license Plate contour and x,y coordinates _, contours, new = cv2.findContours(canny_py(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)Ĭontours = sorted(contours, key=cv2.contourArea, reverse=True) # The code below needs an - or else you'll get a ValueError: too many values to unpack (expected 2) or a numpy error Gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # image = imutils.resize(image, width=500) _cmd = r'C:\Program Files (x86)\Tesseract-OCR\tesseract.exe' There are no errors, it just doesn't read any text. I've tracked the program throughout each line of the code and the OpenCV works fine, but pytesseract won't extract the text from the image.
It find the license plate, extracts a black and white version of it, using the contours, and then when I pass it to pytesseract, it won't read any of the letters.
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