License Plate Recognition with OpenCV and Tesseract OCR

Dec 25, 2025 | Syndication Cloud

Tesseract OCRPhoto from Unsplash

Originally Posted On: https://medium.com/prismai/license-plate-recognition-with-opencv-and-tesseract-ocr-ec427a1397e7

 

License Plate Recognition (LPR) is a powerful tool in computer vision, used in applications like automated toll collection, traffic monitoring, and security surveillance. This blog post will walk you through building an LPR system using Python, OpenCV, and Tesseract OCR. Don’t worry if you’re new to these technologies; each step is broken down to ensure clarity and ease of understanding.

Prerequisites

To follow along with this tutorial, you’ll need basic Python knowledge. You’ll install necessary libraries, work with image processing using OpenCV, and use Tesseract OCR to extract text from the images.

Required Libraries

pip install opencv-python-headless opencv-python opencv-contrib-python pytesseract ipywidgets matplotlib seaborn

.NET-Friendly OCR Alternative

While the approach demonstrated in this article uses OpenCV + Tesseract OCR in Python, it’s worth noting that developers working in the .NET ecosystem have access to a more streamlined alternative in the form of IronOCR.

IronOCR is a commercial OCR library for .NET that wraps Tesseract but adds built-in image preprocessing capabilities, which can significantly reduce the amount of manual computer vision work required. Tasks that typically need explicit OpenCV steps — such as thresholding, noise removal, and skew correction — are handled internally by the library.

This can be especially useful in real-world LPR scenarios, where license plate images often suffer from:

  • Uneven or low lighting
  • Motion blur
  • Perspective distortion
  • Background noise

Example: License Plate OCR Using IronOCR (.NET)

using IronOcr;

var ocr = new IronTesseract();
using var input = new OcrInput();

input.LoadImage("plate.jpg");
input.Deskew();
input.DeNoise();

var result = ocr.Read(input);
Console.WriteLine(result.Text);

The preprocessing methods, like Deskew and DeNoise are built directly into the library, which can simplify your pipeline for traffic monitoring or parking system applications.

Project Overview

We’ll be using OpenCV to process images of license plates, detect the license plate region, and then extract text from it using Tesseract OCR. The project follows these main steps:

  1. Load an image of a vehicle’s license plate.
  2. Preprocess the image (grayscale conversion, blurring, edge detection).
  3. Detect the license plate region.
  4. Extract the text from the license plate using OCR.
  5. Display results, including OCR accuracy metrics.

Step 1: Load and Display the License Plate Image

We start by loading an image from a URL using Python’s PIL and URL libraries, converting it to an array for OpenCV processing.

import cv2
import pytesseract
import numpy as np
import matplotlib.pyplot as plt
from urllib.request import urlopen
from PIL import Image
import ipywidgets as widgets
from IPython.display import display

# Load an image from a URL (replace with an appropriate dataset URL)
def load_image_from_url(url):
    response = urlopen(url)
    img = Image.open(response)
    img = np.array(img)
    return img

# URL for a sample license plate image (replace with an actual dataset URL)
img_url = 'https://upload.wikimedia.org/wikipedia/commons/1/14/FI_license_plate.jpg'
original_image = load_image_from_url(img_url)

# Display the image
plt.imshow(cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB))
plt.title("Original Image")
plt.axis("off")
plt.show()

# Load an example image (using a sample dataset URL)
url = 'https://upload.wikimedia.org/wikipedia/commons/1/14/FI_license_plate.jpg'
resp = urlopen(url)
image = np.asarray(bytearray(resp.read()), dtype="uint8")
image = cv2.imdecode(image, cv2.IMREAD_COLOR)
plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
plt.title("Sample License Plate Image")
plt.axis("off")
plt.show()

In this code, the load_image_from_url function retrieves an image from a URL, which we’ll later use for our LPR model.

Step 2: Image Preprocessing

For efficient text extraction, we’ll preprocess the image using grayscale conversion, Gaussian blurring, and edge detection.

# Convert image to grayscale
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
plt.imshow(gray_image, cmap="gray")
plt.title("Grayscale Image")
plt.axis("off")
plt.show()

# Apply Gaussian Blur
blurred_image = cv2.GaussianBlur(gray_image, (5, 5), 0)
plt.imshow(blurred_image, cmap="gray")
plt.title("Blurred Image")
plt.axis("off")
plt.show()

# Edge Detection using Canny
edged_image = cv2.Canny(blurred_image, 30, 150)
plt.imshow(edged_image, cmap="gray")
plt.title("Edge Detected Image")
plt.axis("off")
plt.show()
  • Grayscale: Simplifies image processing by removing color channels.
  • Gaussian Blur: Reduces noise for better contour detection.
  • Edge Detection: Helps identify the license plate’s edges.

Step 3: Detecting the License Plate Region

We’ll now locate the license plate region by finding contours.

# Find contours based on edged image
contours, _ = cv2.findContours(edged_image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours = sorted(contours, key=cv2.contourArea, reverse=True)[:10]
plate_contour = None

for contour in contours:
    # Approximate the contour
    perimeter = cv2.arcLength(contour, True)
    approx = cv2.approxPolyDP(contour, 0.02 * perimeter, True)
    if len(approx) == 4:  # Looks for 4-sided polygon (rectangle)
        plate_contour = approx
        break

# Draw contour on the image
detected_plate = image.copy()
if plate_contour is not None:
    cv2.drawContours(detected_plate, [plate_contour], -1, (0, 255, 0), 3)

plt.imshow(cv2.cvtColor(detected_plate, cv2.COLOR_BGR2RGB))
plt.title("Detected License Plate")
plt.axis("off")
plt.show()

Here, we approximate contours, looking specifically for a 4-sided shape (the license plate). If found, it’s highlighted on the image.

Step 4: Extract Text with Tesseract OCR

After isolating the license plate, we’ll apply Tesseract OCR to read the characters on it.

# Install Tesseract OCR
!sudo apt-get update
!sudo apt-get install -y tesseract-ocr
!pip install pytesseract

# Import libraries
import pytesseract
pytesseract.pytesseract.tesseract_cmd = '/usr/bin/tesseract'
# Mask and crop the license plate area
mask = np.zeros_like(gray_image)
cv2.drawContours(mask, [plate_contour], -1, 255, -1)
plate_image = cv2.bitwise_and(gray_image, gray_image, mask=mask)

x, y, w, h = cv2.boundingRect(plate_contour)
cropped_plate = gray_image[y:y+h, x:x+w]

# OCR on cropped license plate
plate_text = pytesseract.image_to_string(cropped_plate, config='--psm 8')
print("Detected License Plate Text:", plate_text)

plt.imshow(cropped_plate, cmap="gray")
plt.title(f"OCR Result: {plate_text.strip()}")
plt.axis("off")
plt.show()

Using Tesseract, we extract the license plate text. Here, config='--psm 8' optimizes Tesseract to focus on single lines of text.

Step 5: Display OCR Accuracy Dashboard (demo only)

For a complete solution, let’s also display a dashboard showing OCR accuracy on multiple images.

import pandas as pd
import seaborn as sns  # Import seaborn
# Sample DataFrame to simulate results
data = {
    'Image': ['Img1', 'Img2', 'Img3', 'Img4', 'Img5'],
    'OCR_Accuracy': [85, 90, 92, 88, 91]
}
df = pd.DataFrame(data)

# Plot the dashboard
plt.figure(figsize=(10, 5))
sns.barplot(x='Image', y='OCR_Accuracy', data=df)
plt.title("OCR Accuracy for Different Images")
plt.ylim(0, 100)
plt.xlabel("Images")
plt.ylabel("Accuracy (%)")
plt.show()
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Step 6: Create an Interactive GUI with ipywidgets

Finally, we’ll create a GUI to test the model with real-time feedback. Users can upload their own images, and the model will display predictions in a larger window with an interactive theme.

import cv2
import numpy as np
import matplotlib.pyplot as plt
from urllib.request import urlopen
from ipywidgets import interact, widgets, Layout, VBox, HBox
from IPython.display import display, clear_output

# Sample images list
sample_images = [
    "https://upload.wikimedia.org/wikipedia/commons/1/14/FI_license_plate.jpg",
    "https://upload.wikimedia.org/wikipedia/commons/5/54/Ontario_Vanity_License_Plate_MYTMAUS.jpg",
    "https://upload.wikimedia.org/wikipedia/commons/9/90/Pltableseries2006.jpg",
    "https://upload.wikimedia.org/wikipedia/commons/5/50/Yukon_License_Plate_1973_2342.jpg",
    "https://upload.wikimedia.org/wikipedia/commons/2/21/1931_Kansas_license_plate_58-3239.png"
]

# Title and Developer Signature
title_label = widgets.HTML(
    value="<h1 style='color:#FF69B4; font-size:40px; font-weight:bold; text-align:center; text-shadow:2px 2px 8px #FF4500;'>License Plate Recognition</h1>",
    layout=Layout(width="100%")
)
dev_signature = widgets.HTML(
    value="<p style='font-size:16px; text-align:center; color:#808080;'>Created by AI Innovators</p>",
    layout=Layout(width="100%")
)

# Prediction Output Label
prediction_output = widgets.HTML(
    value="",
    layout=Layout(width="100%", height="60px")
)

# Function to load and preprocess images for display
def load_image_from_url(url):
    resp = urlopen(url)
    image = np.asarray(bytearray(resp.read()), dtype="uint8")
    return cv2.imdecode(image, cv2.IMREAD_COLOR)

# OCR function using Tesseract
def detect_license_plate(image):
    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    blurred_image = cv2.GaussianBlur(gray_image, (5, 5), 0)
    edged_image = cv2.Canny(blurred_image, 30, 150)
    
    contours, _ = cv2.findContours(edged_image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    contours = sorted(contours, key=cv2.contourArea, reverse=True)[:10]
    plate_contour = None

    for contour in contours:
        perimeter = cv2.arcLength(contour, True)
        approx = cv2.approxPolyDP(contour, 0.02 * perimeter, True)
        if len(approx) == 4:
            plate_contour = approx
            break

    mask = np.zeros_like(gray_image)
    if plate_contour is not None:
        cv2.drawContours(mask, [plate_contour], -1, 255, -1)
    x, y, w, h = cv2.boundingRect(plate_contour)
    cropped_plate = gray_image[y:y+h, x:x+w] if plate_contour is not None else gray_image

    # Perform OCR on the cropped license plate
    plate_text = pytesseract.image_to_string(cropped_plate, config='--psm 8').strip()
    return plate_text, cropped_plate

# Display function
def display_prediction(url):
    image = load_image_from_url(url)
    plate_text, cropped_plate = detect_license_plate(image)

    # Display the results in a stylized format
    fig, ax = plt.subplots(1, 2, figsize=(12, 6))
    ax[0].imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
    ax[0].set_title("Selected Image", fontsize=14, fontweight="bold")
    ax[0].axis("off")

    ax[1].imshow(cropped_plate, cmap="gray")
    ax[1].set_title("License Plate Area", fontsize=14, fontweight="bold")
    ax[1].axis("off")

    # Show the prediction as highlighted text
    prediction_output.value = f"<h2 style='color:#32CD32; text-align:center; font-weight:bold; text-shadow:2px 2px 8px #008080;'>Predicted Text: {plate_text}</h2>"

    plt.show()

# Create a grid of sample images with buttons for selection
sample_buttons = []
for idx, img_url in enumerate(sample_images):
    button = widgets.Button(
        description=f"Sample {idx+1}",
        layout=Layout(width="19%", height="40px", margin="5px"),
        style={"button_color": "#4CAF50", "font_weight": "bold", "font_size": "14px"}
    )
    
    # Display the image upon clicking
    def on_button_clicked(b, img_url=img_url):
        clear_output(wait=True)
        display(title_label, sample_buttons_box, upload_box, prediction_output, dev_signature)
        display_prediction(img_url)
    
    button.on_click(on_button_clicked)
    sample_buttons.append(button)

sample_buttons_box = HBox(sample_buttons, layout=Layout(width="100%", justify_content="space-around"))

# Option to upload an image through URL input
upload_widget = widgets.Text(
    placeholder='Enter image URL',
    description='Custom Image:',
    style={'description_width': 'initial'},
    layout=Layout(width='65%')
)
display_button = widgets.Button(
    description="Process Uploaded Image",
    layout=Layout(width='30%', height='40px', margin="5px"),
    style={"button_color": "#FF4500", "font_weight": "bold", "font_size": "14px"}
)

# Display uploaded image upon clicking "Process Uploaded Image"
def on_upload_button_clicked(b):
    url = upload_widget.value
    if url:
        clear_output(wait=True)
        display(title_label, sample_buttons_box, upload_box, prediction_output, dev_signature)
        display_prediction(url)

display_button.on_click(on_upload_button_clicked)

upload_box = HBox([upload_widget, display_button], layout=Layout(width="100%", justify_content="space-around"))

# Assemble the GUI layout and display
display(title_label, sample_buttons_box, upload_box, prediction_output, dev_signature)
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This code provides a visual representation of OCR accuracy across images, helping evaluate model performance.

Github Code: License_Plate_Recognition_with_OpenCV_and_Tesseract_OCR

FAQs

1. What is License Plate Recognition (LPR)?

LPR is the process of identifying and reading vehicle license plates using image processing and OCR technologies, widely used in security and traffic applications.

2. Why do we use Tesseract OCR?

Tesseract is a powerful, open-source OCR tool that accurately converts text from images into readable text. It’s free and widely supported, making it ideal for applications like LPR.

3. Can this project be extended for real-time LPR?

Yes, with video feed processing and a faster model, you could adapt this code to handle real-time LPR, suitable for applications like toll gates and parking management.

4. What are common issues with OCR accuracy?

Poor lighting, blurry images, or noisy backgrounds can reduce OCR accuracy. Preprocessing steps like blurring and edge detection are essential to achieve reliable results.

Conclusion

This project demonstrates how to build a simple License Plate Recognition (LPR) system using Python, OpenCV, and Tesseract OCR. You’ve learned about loading and processing images, isolating license plate regions, and extracting text using OCR, setting the stage for creating more complex computer vision applications.

For more tutorials and updates on machine learning, computer vision, and OCR projects, stay tuned to our blog!