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From there, we’ll review our project structure. In the first part of this tutorial, you’ll learn and define what Automatic License/Number Plate Recognition is. Such a system could help reduce speeding violations and create better neighborhood safety.
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If the car exceeds the speed limit, you can analyze the license plate, apply OCR to it, and log the license plate number to a database. Or maybe you want to build a camera-based (radar-less) system that determines the speed of cars that drive by your house using a Raspberry Pi. Using a bit of OpenCV, Python, and Tesseract OCR knowledge, you could help your homeowners’ association monitor cars that come and go from your neighborhood. In this tutorial, my goal is to teach you one of the quickest ways to build such an Automatic License/Number Plate Recognition system. I knew which image processing techniques the developers used to automatically localize my license plate in the image and extract the plate number via OCR. I knew exactly how their Automatic License/Number Plate Recognition system worked. There is was, clear as day! You could see the license plate number on my old Honda Civic (before it got burnt to a crisp in an electrical fire.) That speeding camera caught me with my foot on the pedal, quite literally, and it had the pictures to prove it too. Sure enough, I had unknowingly driven past a speed-trap camera doing 78 MPH in a 65 MPH zone. Two weeks later … I got the speeding ticket in the mail. I didn’t even notice when I drove past a small gray box discreetly positioned along the side of the highway.
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Of course, I had my windows down, my music turned up, and I had totally zoned out - not a care in the world. My first run-in with ANPR was about six years ago.Īfter a grueling three-day marathon consulting project in Maryland, where it did nothing but rain the entire time, I hopped on I-95 to drive back to Connecticut to visit friends for the weekend.
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Looking for the source code to this post? Jump Right To The Downloads Section OpenCV: Automatic License/Number Plate Recognition (ANPR) with Python
#Anpr open source how to
To learn how to build a basic Automatic License Plate Recognition system with OpenCV and Python, just keep reading. By the end of this guide, you’ll have a template/starting point to use when building your own ANPR projects. In this tutorial we’ll be building a basic Automatic License/Number Plate Recognition system. Because of that, it’s often not the trained model that is valuable, but instead the dataset that a given company has curated.įor that reason, you’ll see ANPR companies acquired not for their ANPR system but for the data itself! ANPR contracts with local and federal governments tend to be highly competitive.ANPR datasets are tedious to curate, requiring an incredible investment of time and staff hours to annotate.These datasets contain sensitive identifying information related to the vehicle, driver, and location.Several compounding factors make ANPR incredibly challenging, including finding a dataset you can use to train a custom ANPR model! Large, robust ANPR datasets that are used to train state-of-the-art models are closely guarded and rarely (if ever) released publicly:
It needs to be able to detect the license plate of each car passing by, OCR the characters on the plate, and then store this information in a database so the owner of the vehicle can be billed for the toll.