Get high-accuracy, developer-friendly automatic license plate recognition (ALPR) or automatic number plate recognition (ANPR) software! The core of our ALPR, ANPR system is based on state of the art deep neural networks architectures.
- Reading License Plates from Images
- Number Plate Recognition on a Video or Camera Stream
- Automatic Image Transfer
- Code Samples
Our machine-learning software:
- Works on dark, low-res, blurry images and tough angles, all vehicle types, etc. See our full ALPR results.
- Decodes license plate , vehicle type (e.g. SUV, van, pickup truck), vehicle make model (e.g. Honda Accord), color, and orientation. Ignores bumper stickers, car signs, etc.
- Is optimized for all 50 USA States, India, Brazil and 90+ countries worldwide.
Snapshot : Get license plate reader from images in under 60 minutes:
- Access a simple REST API for easy integration in 8 programming languages.
- Returns results via JSON Response or Webhooks.
- Has fast inference speed up to 21 ms.
- Runs on-premise on Linux, Windows , Mac, Jetson , Kubernetes, Raspberry Pi, Zynq, 96Boards, LattePanda and more.
Stream : Get number plate recognition from camera or video feed:
- Returns license plate results via CSV file or Webhooks.
- Handles 4 cameras simultaneously on mid-range PC.
- Runs on-premise on Linux, Windows , Mac and Jetson.
ALPR, ANPR software is ideal for parking, highway monitoring, toll, police surveillance, community security, and other use cases. Our license plate recognition (LPR) software can also forward results to our full ALPR Dashboard and Parking Management software solution, ParkPow. Sign up for a Free Trial now (no credit card required) or learn more at https://platerecognizer.com.
Get your API key from Plate Recognizer. Replace MY_API_KEY with your API key and run the command below.
For setup instructions of the script, checkout our guides here
# Getting started!
git clone https://github.com/parkpow/deep-license-plate-recognition.git
cd deep-license-plate-recognition
pip install requests pillow
python plate_recognition.py --api-key MY_API_KEY /path/to/vehicle.jpg
The result includes the bounding boxes (rectangle around object) and the plate value for each plate. View the details of the results on our documentation.
[
{
"version": 1,
"results": [
{
"box": {
"xmin": 85,
"ymin": 85,
"ymax": 211,
"xmax": 331
},
"plate": "ABC123",
"score": 0.904,
"dscore": 0.92
}
],
"filename": "car.jpg"
}
]You can match the license plate patterns of a specific region.
python plate_recognition.py --api-key MY_API_KEY --regions fr --regions it /path/to/car.jpg
You can also run the license plate reader on many files at once. To run the script on all the images of a directory, use:
python plate_recognition.py --api-key MY_API_KEY /path/to/car1.jpg /path/to/car2.jpg /path/to/trucks*.jpg
To use a locally hosted sdk, pass the url to the docker container as follows:
python plate_recognition.py --sdk-url http://localhost:8080 /path/to/vehicle.jpg
The script number_plate_redaction.py differs from the default prediction script because it also detects plates that are barely readable and/or very small. It returns the bounding boxes of all the license plates. They can be used to blur or mask the license plates.
In addition, it can split the image in smaller images to better deal with high resolution images. This will use 3 lookups instead of just 1. To use this options add --split-image.
The option --save-blurred lets you save blurred images. They are saved to a new file with _blurred suffix.
The option --ignore-regexp lets you specify a regex of plates to ignore from blur. This option can be specified multiple times.
The option --ignore-no-bb lets you ignore recognitions without a vehicle bounding box from blur.
python number_plate_redaction.py --help
python number_plate_redaction.py --api-key API_KEY vehicels.jpg
python number_plate_redaction.py --sdk-url http://localhost:8080 --split-image vehicels.jpg
python number_plate_redaction.py --api-key 77c### 58C5A57_14965463.jpg --save-blurred --ignore-regexp ^58c5a57$ --ignore-regexp ^[0-9][0-9]c5a57$
You can send images directly to our FTP. See our FTP integration for details.
If you want to use your own FTP or SFTP server, another option is to process images with ftp_and_sftp_processor.py:
For setup instructions of the script, checkout our guides here
python ftp_and_sftp_processor.py --api-key MY_API_KEY --hostname FTP_HOST_NAME --ftp-user FTP_USER --ftp-password FTP_USER_PASSWORD --folder /path/to/server_folder
By default the script is prepared to process images in FTP servers, if you want to process images in an SFTP server, add the argument --protocol sftp.
To remove images from FTP or SFTP server after processing, add the argument --delete.
| Arguments | Description |
|---|---|
| -h, --help | Show help message |
| -a, --api-key | Your API key. |
| -r, --regions | Match the license plate pattern for a specific region. |
| -s, --sdk-url | URL to self-hosted SDK. For example, http://localhost:8080 |
| -c, --protocol | Protocol to use, available choices 'ftp'(default) or 'sftp' |
| -t, --timestamp | Timestamp. |
| -H, --hostname | Host. |
| -p, --port | Port |
| -U, --ftp-user | Transfer protocol server user |
| -P, --ftp-password | Transfer protocol server user's password |
| -d, --delete | Remove images from the FTP server after processing. Optionally specify a timeout in seconds. |
| -f, --folder | Specify a folder with images on the FTP server. |
| -o, --output-file | Save the result to a file. |
| -i, --interval | Periodically fetch new images from the server every interval seconds. |
| --camera-id | Name of the source camera. |
| --cameras-root | Root folder containing dynamic cameras. |
| --format | Format of the result, available choices 'json'(default) or 'csv' |
| --mmc | Predict vehicle make and model (SDK only). It has to be enabled. |
| --pkey | SFTP Private Key Path. |
Automatic Image Transfer is a command line tool that runs our ALPR Engine. It monitors a folder and automatically process images (Cloud or SDK) as they are added. Once processed, images are moved to an archive directory. It can also forward the results to our parking management service Parkpow.
To get started: python transfer.py --help
See our sample projects to easily get started with the API.
- Example program in C++.
- Example program in C#.
- Example program in Java.
- Android App. It lets you take a picture and send it to our API.
- Android App in Kotlin.
- View how to integrate with other languages in our documentation.
- A Node-RED node for license plate recognition.
- Or convert the curl examples to other languages.
Have questions? Let us know how we can help.
Provided by Plate Recognizer, a subsidiary of ParkPow.