🧠 Plagiarism Analyzer

A Django-based web application that detects plagiarism by analyzing the similarity between documents using Natural Language Processing (NLP) techniques.
This project helps educators, students, and researchers identify copied or highly similar content across text submissions.

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🚀 Features

• 📂 Upload one or multiple text files for comparison
• 🔍 Calculates text similarity using NLP and cosine similarity
• 🧹 Performs preprocessing: tokenization, stopword removal, and stemming
• 📊 Displays detailed similarity percentage between documents
• 🧾 Generates clean and readable plagiarism reports
• 🌐 Simple and responsive web interface built with HTML, CSS, and Django templates

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🏗️ Project Structure

PlagiarismAnalyzer/
├── analyzer/      # Django app for text comparison logic
├── static/       # CSS, JS, and image files
├── templates/     # HTML templates
├── requirements.txt  # Python dependencies
├── manage.py     # Django management script
└── README.md     # Project documentation

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🧩 Technologies Used

• Python 3.x
• Django Framework
• NLTK (Natural Language Toolkit)
• HTML, CSS (Frontend)
• SQLite / MySQL (Database)

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⚙️ Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/vasanthan2507/PlagiarismAnalyzer.git
cd PlagiarismAnalyzer

2️⃣ Create a Virtual Environment

python -m venv env
source env/bin/activate  # On macOS/Linux
env\Scripts\activate    # On Windows

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Download Required NLTK Packages

Run this in Python shell:
import nltk
nltk.download('punkt')
nltk.download('stopwords')

5️⃣ Run the Development Server

python manage.py runserver

Then open your browser and go to:
👉 http://127.0.0.1:8000/

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🧮 How It Works

1. User uploads two or more text files.
2. The system preprocesses the text (lowercasing, stopword removal, stemming).
3. Texts are converted into vectors using token frequency.
4. Cosine similarity is computed between document pairs.
5. A similarity score (%) is displayed to indicate possible plagiarism.

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📈 Future Enhancements

• 🔸 Support for PDF and DOCX file formats
• 🔸 Sentence-level plagiarism highlighting
• 🔸 REST API for external integration
• 🔸 User authentication system (Admin / Teacher / Student roles)
• 🔸 Report export as PDF

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👨‍💻 Author

Vasanthan
🎓 MCA Student, SMVEC
💡 Passionate about Python, Data Analytics, and Machine Learning
🌍 GitHub: https://github.com/vasanthan2507

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🪪 License

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

⭐ If you like this project, give it a star on GitHub!