Our algorithms are trained withfrom over 100 countries worldwide.
Different states & countries have different plate formats and styles. Our algo is tuned to your specific location.
Everyone says they are high in accuracy. Read below to see why ours is better.
Our SDK has an inference speed of 100 ms on a mid-range CPU. Our API Cloud returns within 200 ms at 95% of the time.
Our software works via the cloud or on your local server with no Internet required.
Unlike other providers, our engineers (not a random customer service rep) personally respond back to your support questions.
Works on Blurry Images
Works When Plate is at an Angle
Works When Vehicle is Driving Fast
Works on Plates with Icons
Works in Dark Environment
Works with Multiple Vehicles
Works on Low-Res Images
Works with Motorcycles, Buses, etc.
Works on Plates with 2 Rows
# Get an image
curl -o /tmp/car.jpg https://platerecognizer.com/static/demo.jpg
# Call the API
curl -F 'upload=@/tmp/car.jpg' -H 'Authorization: Token 123456' https://platerecognizer.com/v1/plate-reader
"xmin": 12, "ymin": 84, "ymax": 168, "xmax": 380},
"score": 0.90 }]
Integrate with our LPR API in a few lines of code and get an easy to use JSON response with the number plate value of the vehicle.
See examples of how to interface with our API on this Github . You can call the API on all the files of a directory and analyze the frames of a video.
Sign up to access our API or download our SDK.
Five is Better than One. Unlike other LPR providers, we can return up to 5 decoded license plates from one single image. We achieve this by utilizing two distinct neural networks. One identifies all the photos of license plates from an image and the other decodes each character of the plate.
Multiple Plate Styles. Let’s face it–not all plates are the same. Some have two rows of text. Some have icons at the beginning, middle or end of the plate. Some contain fancy fonts. Thankfully, our LPR engine supports them all.
Environment Matters. It’s easy to decode a license plate from a high-res photo of a vehicle on a sunny day. But we know that in reality, the weather and other conditions are not always ideal. To that end, we have been relentlessly enhancing our algorithms to support the variety of “real-life” factors. such as sun glare, blurry images, fast vehicles, night-time, and many more.
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