license-plate-recognition

<p align="center"> <a href="https://play.google.com/store/apps/dev?id=7086930298279250852" target="_blank"> <img alt="" src="https://github-production-user-asset-6210df.s3.amazonaws.com/125717930/246971879-8ce757c3-90dc-438d-807f-3f3d29ddc064.png" width=500/> </a> </p>

Product & Resources

For more information, visit KBY AI's GitHub Repository.

Automatic License Plate Recognition-Docker

Overview

This repository demonstrates ANPR/ALPR(Automatic Number/License Plate Recognition) SDK with unmatched accuracy and precision by applying SOTA(State-of-the-art) deep learning techniques. </br> KBY-AI's LPR solutions utilizes artificial intelligence and machine learning to greatly surpass legacy solutions. Now, in real-time, users can receive a vehicle's plate number through API. The repository includes features that allow for testing LPR against images using both image files and base64-encoded images.

We can customize the SDK to align with customer's specific requirements.

Docker Run

• Pull docker image.

sudo docker pull kbyai/license-plate-recognition:latest

• Run docker container with license key file.

sudo docker run -v ./license.txt:/home/openvino/kby-ai-alpr/license.txt -p 8081:8080 -p 9001:9000 kbyai/license-plate-recognition:latest

SDK License

This project demonstrates KBY-AI's License Plate Number Recognition SDK, which requires a license for each machine or instance on which it runs.

• To get the machine code for license, use the following syntax:

sudo docker run -e LICENSE="xxxxx" kbyai/license-plate-recognition:latest

• To request the license, please provide us with the machine code obtained from your server. <img alt="Postman" src="https://github.com/user-attachments/assets/73861421-4ce8-4d01-a5df-73c14eb0626f" width=800/>

Contact us:</br> 🧙Email: [email protected]</br> 🧙Telegram: @kbyaisupport</br> 🧙WhatsApp: +13348402323</br> 🧙Discord: KBY-AI</br> 🧙Teams: KBY-AI</br>

Online Demo

This SDK can be tested on online test demo page here:

Please select tab 'ALPR/ANPR` for this SDK

Gradio Demo

To test the Gradio demo from the Docker image, access it at http://127.0.0.1:9001 or http://localhost:9001.

Postman

The API can be evaluated through Postman tool. Here are the endpoints for testing:

• Test with an image file: Send a POST request to http://127.0.0.1:8081/alpr.
• Test with a base64-encoded image: Send a POST request to http://127.0.0.1:8081/alpr_base64.