🚘 Automatic License Plate Recognition (ALPR) using Docker

This is a complete AI-powered Automatic License Plate Recognition (ALPR) system built with Python and OpenCV, packaged with Docker for easy deployment. It performs real-time or image-based number plate detection and character recognition using deep learning and OCR. Ideal for smart surveillance, parking systems, toll booths, or traffic enforcement systems.

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🔧 Tech Stack

📦 Frameworks & Libraries

• OpenCV – Image processing and contour detection
• Pytesseract – Optical Character Recognition (OCR)
• NumPy – Numerical operations on arrays
• Matplotlib – Visualizing intermediate steps (optional)
• Flask (optional) – For building a RESTful API (if using web frontend)

🐳 Tools & Platforms

• Docker – Containerized deployment
• Tesseract OCR – External dependency for text recognition
• Git & GitHub – Version control and collaboration

🖥️ Frontend (Optional)

• HTML/CSS/JavaScript – Simple UI for uploading images (if web-based)
• Streamlit – For building an interactive Python dashboard (optional)

🔙 Backend

• Python – Main programming language for image analysis and OCR
• Flask – If building as an API backend
• Docker – To isolate and deploy the app easily

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📊 Data Collection Process

📸 Input Data

• Images of vehicles collected from:

  • Google Images
  • Kaggle datasets
  • Real-time camera footage
  • Custom dataset manually labeled for fine-tuning

📂 Preprocessing Steps:

• Convert image to grayscale
• Apply noise reduction
• Use edge detection (Canny)
• Find contours and extract number plate region
• Resize and prepare for OCR

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⚙️ Setup & Usage

1️⃣ Clone the Repository

git clone https://github.com/your-username/Automatic-License-Plate-Recognition-Docker.git
cd Automatic-License-Plate-Recognition-Docker

2️⃣ Build and Run with Docker

docker build -t alpr-app .
docker run -it --rm -v ${PWD}/input:/app/input alpr-app

Make sure to place your vehicle images inside the input/ folder before running.

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🧠 How It Works

1. Image Preprocessing

   • Convert to grayscale
   • Edge detection using Canny

2. License Plate Detection

   • Contour analysis to find plate-like regions

3. OCR Recognition

   • Tesseract extracts text from the detected plate area

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📁 Folder Structure

Automatic-License-Plate-Recognition-Docker/
├── Dockerfile
├── app.py
├── input/            # Folder to store input images
├── output/           # Detected plates and extracted text
├── requirements.txt
└── README.md

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🔧 Requirements (for local run without Docker)

• Python 3.x
• OpenCV
• Pytesseract
• Tesseract OCR (installed on system)

Install dependencies:

pip install -r requirements.txt

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📷 Example

Input Image: input_car.jpg

Detected Plate Output: TN01AB1234

(Optional: Add image examples in the repo's examples/ folder.)

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🛡️ Use Cases

• Smart Parking Management Systems
• Toll Booth Automation
• Traffic Rule Enforcement
• Access Control in Secure Zones
• RTO Vehicle Verification

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📝 License

This project is open-source and available under the MIT License.