Car-Plate-Detector

Project Overview:

Developing a License Plate Recognition (LPR) System

Project Objectives:

-License Plate Localization:

Identify and extract the license plate region from an image.

-License Plate Type Classification:

Create a deep learning model to determine the type of the license plate (e.g., standard, non-standard).

-License Plate Color Identification:

In some cases, recognize and classify the color of the license plate.

-Video Processing:

Extend the project's functionality to work with videos by dividing them into individual frames for processing.

Skills and Technologies Used:

-Deep Learning Framework:

Keras and TensorFlow for creating the neural network models.

-Image Processing Libraries:

OpenCV, Pillow (PIL), Matplotlib, Colorsys, Scikit-image for image processing and visualization.

-Data Processing:

Scikit-learn (SKlearn) for data manipulation.

-License Plate Localization Model:

YOLO (You Only Look Once) algorithm for detecting the position of license plates in images.

-License Plate Type Classification:

Using a multi-task model for this purpose.

-Color Identification:

Using a symmetric algorithm for color detection.

-Video Processing:

Dividing videos into frames for image processing.

Development Process:

-Image Preprocessing:

Use OpenCV and other libraries to prepare the input images, including resizing, cropping, and enhancing image quality.

-License Plate Localization:

Utilize the YOLO algorithm to identify and extract license plate regions from images.

-License Plate Type Classification:

Train a deep learning model using Keras and TensorFlow to classify the type of license plates. You might consider convolutional neural networks (CNNs) for this task.

-License Plate Color Identification:

Implement a color detection algorithm, which might involve image segmentation and color analysis techniques.

-Video Processing:

Extend the code to process videos by splitting them into frames and applying the same image processing and analysis methods.

-Testing and Evaluation:

Evaluate the model's performance using appropriate metrics and fine-tune it for accuracy.

-Deployment:

Integrate the system into your desired application or platform.

-Documentation and Maintenance:

Ensure proper documentation of the project and maintain it for future improvements.