License Plate Detection and Sentiment Analysis Project

Overview

This repository contains two main components: an Automatic Number Plate Recognition (ANPR) system and a Sentiment Analysis implementation. The project demonstrates the application of computer vision and natural language processing techniques in real-world scenarios.

Project Structure

.
├── Plate Number Detection/
│   └── Final-model-ANPR/
│       ├── Character_Detector.ipynb
│       ├── LicensePlate_Detector.ipynb
│       ├── LicensePlate_Detector_YOLO12n.ipynb
│       ├── OCR.ipynb
│       └── final_test_result/
└── Sentiment Analysis/
    ├── P1 & P2/
    └── P3/

1. License Plate Detection (ANPR)

Description

The ANPR system is designed to automatically detect and recognize vehicle license plates from images or video streams. The system employs a multi-stage approach:

1. License Plate Detection: Utilizes YOLO-based models for accurate plate localization
2. Character Detection: Implements character segmentation within detected plates
3. Optical Character Recognition (OCR): Converts detected characters into machine-readable text

Components

• Character_Detector.ipynb: Implementation of character segmentation and detection
• LicensePlate_Detector.ipynb: Main license plate detection module using YOLOv11n architecture
• LicensePlate_Detector_YOLO12n.ipynb: Enhanced detection using YOLOv12n architecture
• OCR.ipynb: Optical Character Recognition implementation

2. Sentiment Analysis

Description

The sentiment analysis component focuses on natural language processing to determine sentiment polarity in text data. The implementation is divided into multiple phases:

• Phase 1 & 2: Initial implementation and baseline models
• Phase 3: Advanced sentiment analysis techniques and improvements

Features

• Text preprocessing and cleaning
• Sentiment classification
• Performance evaluation metrics
• Model optimization

Contributing

Contributions are welcome!

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• YOLOv12 and YOLO11 implementation contributors
• OpenCV community
• NLTK developers