# How to Develop the McCance ANPR Installation System Using Python and Machine Learning

Automatic Number Plate Recognition (ANPR) systems are transforming how vehicles are tracked and monitored across highways, toll booths, private premises, and city surveillance grids. For a company like [**McCance ANPR Installation System**](https://www.mccancehighways.co.uk/our-services/anpr-installation), integrating a powerful ANPR system into its infrastructure services can offer real-time vehicle identification, enhance security, and enable intelligent traffic analytics.

In this blog, we’ll walk you through how to develop a McCance-branded ANPR system using **Python** and **Machine Learning**, with practical steps, tools, and best practices.

---

## ✅ What Is ANPR?

ANPR (Automatic Number Plate Recognition) is a technology that uses optical character recognition (OCR) to read vehicle registration plates in real-time. The system typically comprises:

* **Camera Modules** (IR, CCTV)
    
* **Edge Device / Server**
    
* **Computer Vision Software (Python)**
    
* **Machine Learning Model** (OCR + Preprocessing)
    
* **Database & APIs**
    

For McCance, an ANPR system can be installed at key lighting poles, traffic intersections, or tolling points, integrated with existing infrastructure.

---

## 🧰 Tools and Libraries Needed

To build the system, you’ll need the following:

### Python Libraries:

* **OpenCV** – Image processing
    
* **EasyOCR** or **Tesseract OCR** – Character recognition
    
* **NumPy** – Array and matrix operations
    
* **TensorFlow/Keras or PyTorch** – For training custom models (optional)
    
* **Flask or FastAPI** – For exposing API endpoints
    
* **SQLite or PostgreSQL** – For storing plate data
    
* **Pandas** – Data manipulation
    

### Hardware:

* HD cameras (night vision supported)
    
* Raspberry Pi or Jetson Nano (for edge computing)
    
* Lighting poles for mounting (McCance infrastructure)
    

---

## 🧠 Step-by-Step Guide to Building McCance ANPR

---

### **1\. Capture Video Input from Camera**

Install cameras at optimal angles and heights, considering both lighting and vehicle speed. Use OpenCV to stream real-time footage:

```plaintext
pythonCopyEditimport cv2

cap = cv2.VideoCapture(0)  # Use video file path for recorded feeds

while True:
    ret, frame = cap.read()
    if not ret:
        break
    cv2.imshow("Live Feed", frame)
    if cv2.waitKey(1) == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()
```

---

### **2\. Detect License Plates Using Object Detection**

Use OpenCV with Haar cascades or YOLOv5 for better detection accuracy:

```plaintext
pythonCopyEdit# YOLOv5 license plate detection
from ultralytics import YOLO

model = YOLO("yolov5_license_plate.pt")  # Pretrained or custom model
results = model.predict(source=frame)
```

You can also use pre-trained license plate detection models from Roboflow or train your own using annotated data.

---

### **3\. Crop Plate Region for OCR**

Once the license plate is detected:

```plaintext
pythonCopyEditfor result in results.xyxy[0]:  # x1, y1, x2, y2
    x1, y1, x2, y2 = map(int, result[:4])
    plate_img = frame[y1:y2, x1:x2]
    cv2.imwrite("plate.jpg", plate_img)
```

This cropped image is fed to the OCR model.

---

### **4\. Extract Text Using OCR**

Use EasyOCR for its high accuracy on different fonts:

```plaintext
pythonCopyEditimport easyocr

reader = easyocr.Reader(['en'])
text = reader.readtext('plate.jpg', detail=0)
print("Detected Plate Number:", text)
```

Tesseract is also a solid option, but EasyOCR typically handles distorted images and varied fonts better.

---

### **5\. Store Recognized Plates in Database**

You can store captured data with timestamps and camera IDs:

```plaintext
pythonCopyEditimport sqlite3
import datetime

conn = sqlite3.connect('plates.db')
cursor = conn.cursor()

cursor.execute('''
    CREATE TABLE IF NOT EXISTS plates (
        id INTEGER PRIMARY KEY AUTOINCREMENT,
        plate_text TEXT,
        timestamp TEXT,
        location TEXT
    )
''')

cursor.execute('''
    INSERT INTO plates (plate_text, timestamp, location)
    VALUES (?, ?, ?)
''', (text[0], datetime.datetime.now(), "Hampshire Junction 7"))

conn.commit()
conn.close()
```

---

### **6\. Build API with Flask or FastAPI**

To allow web or mobile apps to access data:

```plaintext
pythonCopyEditfrom flask import Flask, jsonify

app = Flask(__name__)

@app.route("/plates", methods=["GET"])
def get_plates():
    conn = sqlite3.connect('plates.db')
    cursor = conn.cursor()
    cursor.execute("SELECT * FROM plates ORDER BY timestamp DESC")
    records = cursor.fetchall()
    conn.close()
    return jsonify(records)

app.run(port=5000)
```

This will help McCance staff or clients monitor the vehicle logs via web dashboards.

---

### **7\. Add Real-Time Alerts or Flags**

You can match recognized plates with a blacklist or whitelist:

```plaintext
pythonCopyEditblacklist = ['MH12AB1234', 'DL8CAF0000']

if text[0] in blacklist:
    print("🚨 Blacklisted vehicle detected!")
    # Trigger alarm or webhook
```

You can also integrate this with SMS/email notifications or local alarms via Raspberry Pi GPIO pins.

---

## 🧪 Optional: Train Custom OCR for Local Plates

If standard OCR doesn’t work well due to regional fonts, try training your own OCR:

1. Annotate images using LabelImg or Roboflow.
    
2. Train using CRNN (Convolutional Recurrent Neural Network) in Keras or PyTorch.
    
3. Export model and load it via ONNX or TorchScript for deployment.
    

---

## 📈 Use Cases for McCance's ANPR System

* **Toll Booth Automation** – Capture and bill based on number plates.
    
* **Parking Management** – Recognize and allow entry/exit of registered vehicles.
    
* **Traffic Law Enforcement** – Flag stolen or unregistered vehicles.
    
* **Smart Lighting Integration** – Activate lights based on motion + plate recognition.
    

---

## 🚀 Deployment Tips for McCance Engineers

* **Weatherproof Cameras** – Use IP66-rated cases for outdoor installation.
    
* **IR Lighting** – Ensure night-time visibility.
    
* **Edge Processing** – Run models on Raspberry Pi or NVIDIA Jetson for low latency.
    
* **Remote Updates** – Use OTA (over-the-air) methods for software updates.
    
* **Logging** – Store daily logs locally + backup to the cloud (AWS S3 or GCP).
    

---

## 📌 Challenges & Solutions

| Challenge | Solution |
| --- | --- |
| Low-light conditions | Use IR cameras and add LED floodlights |
| Non-standard plate fonts | Train custom OCR using local data |
| Real-time processing bottlenecks | Use edge computing with Jetson or multi-threading in Python |
| Privacy & compliance | Anonymize or encrypt data, follow GDPR/local policies |

---

## 🏁 Conclusion

Developing a robust ANPR system for [**McCance’s ANPR Installation System**](https://www.mccancehighways.co.uk/our-services/anpr-installation) using Python and Machine Learning is not only feasible but highly scalable. By leveraging open-source libraries like OpenCV and EasyOCR, and deploying models on low-cost edge devices, you can build a smart, responsive system that adds massive value to your roadside solutions.

With real-time alerts, detailed logs, and AI-powered recognition, McCance’s ANPR installations can help bring smart cities to life.