Plate Recognizer Snapshot

ALPR Works in Real Life

  • Blurry images
  • Low-res images
  • Dark images
  • Small images
  • Vehicles at an angle
  • Vehicles driving fast
  • Vehicles far away
  • Multiple vehicles
  • Plates with 2 rows
  • Plates with icons
  • Plates with stacked characters
  • Different colored plates
  • Motorcycles, buses, trucks
  • Vans, trailers, long-haul trucks
  • Unique, colored fonts
  • Mixed fonts

Input Parameters

  1. Image.  Send an image in a variety of formats.

  2. Region. Specify the region(s) or leave it blank to use our global ANPR engine.

  3. Camera. Specify the camera associated with the image.

  4. Timestamp. Optional.

Output Parameters

  1. Decoded Plate.  See license plate(s) decoded by engine.

  2. Bounding Box. Get x, y coordinates of box around each license plate.

  3. Score. See engine’s confidence level in correctly reading plate characters.

  4. Dscore. Understand engine’s confidence level that a plate is found.

  5. Additional Candidates.  See additional decoded plate possibilities with score.

  6. Region of Plate. Get the region code of decoded vehicle.

  7. Vehicle Type. Know if vehicle is ambulance, bus, car, limousine, motorcycle, taxi, truck, van, or unknown.

  8. Vehicle Make/Model.  See make & model from over 9,500 different models.

  9. Vehicle Color. Get color of vehicle.

API Cloud

  • Requires Internet connection.
  • Supports images up to 3 MB.
  • Limit of 8 images per second.
  • Runs on Linode Cloud servers in New Jersey, USA.

On-Premise SDK

  • No Internet required, except during set-up.
  • No image file size limitation.
  • No limit on # lookups per second.
  • Runs on Windows, Linux, Mac, Pi, Jetson Nano and more.
  • Operates on a Docker container with REST API.
  • Installs easily with SDK Manager tool.
  • Supports Kubernetes architecture.
  • See details and hardware requirements for SDK.

Ways to Deploy Plate Recognizer Snapshot

API Integration

  • Send images to our API Cloud or SDK.
  • Incorporate ALPR output into your app.
  • See Sample Plate Rec Code in 8 languages.

Video Mgmt Software


  • We’re pre-integrated with Axis and Meraki cameras.
  • Upon motion detection, camera sends image to our API Cloud.


Simple REST API for ALPR

Integrate with our ALPR API with a few lines of code (Python, Javascript, Shell, Ruby, C#, etc.) 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 project. 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.

Get Started

Get Fast ALPR Quickly

Get ANPR up and running in under 60 minutes. Free Trial with no Credit Card required!

Get Started

Online Dashboard

  • Get full list of all vehicles decoded.
  • Search for a specific license plate.
  • Filter results based on camera, start time, end time, or confidence level.
  • Star or delete entry to more easily manage dashboard.
  • Data stored for 7 days.
  • More features coming soon!

Online Dashboard available only for API Cloud customers. 


  • Easily create webhooks for API Cloud or SDK to “auto-forward” ANPR output.
  • Construct one or multiple webhooks per API Cloud account or SDK license.
Learn More

License Management

  • Easily see all products purchased.
  • Describe each SDK license to keep track of them.
  • See lookup usage for each license.
  • Move license to another device.
  • Activate or disable a license key.

Supported Devices for Snapshot SDK

  • Windows
  • Linux
  • Macbook
  • Raspberry Pi 3 & 4
  • Nvidia Jetson Nano, TX1, TX2, Xavier NX
  • Orange Pi
  • LattePanda

SDK Software is executed on a Docker Container.

See HW Requirements

Languages Supported

See Sample Code

Simple, Subscription-Based Pricing

Plate Recognizer Snapshot is priced as a monthly or annual fee based on # Lookups per month. Vehicle Make, Model and Color are priced additionally.

Perpetual, one-time license fee available upon request — for situations where there’s absolutely no Internet connection.

Take advantage of our Free Trial and start today!

See Pricing

Inference Speeds for Snapshot SDK

Inference Speed (ms)Fast ModeRegular Mode
n1-standard-4 (4 vCPUs, 15 GB RAM),
1 x NVIDIA Tesla T4
n2-standard-8 (8 vCPUs, 32 GB RAM)2141
c2-standard-4 (4 vCPUs, 16 GB RAM)3058
n2-standard-4 (4 vCPUs, 16 GB RAM)3364
n1-standard-4 (4 vCPUs, 15 GB RAM)4794
n2-standard-2 (2 vCPUs, 8 GB RAM)58108
e2-standard-2 (2 vCPUs, 8 GB RAM)77145
Intel Core i7-8550U CPU @ 1.80GHz3355
Raspberry Pi 3/410001300
LattePanda Alpha119170
LattePanda V18751250
Nvidia Jetson Nano250300
* Speeds based on HD Image size (1280 x 720).  Fast Mode can be used in situations where vehicle size is at least 10% of image size.  Otherwise, Regular Mode is recommended.  For details, go to our GitHub page on ALPR Speeds.

Need Help with ALPR?

Let us know and we’d be happy to assist.

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