Plagiarism Detector

A machine learning-based application designed to detect plagiarism in text documents. This tool analyzes text content and identifies potential similarities using natural language processing (NLP) techniques. A robust and user-friendly Chrome extension powered by a machine learning model to detect plagiarism in text. The extension integrates a Flask-based backend and leverages a pre-trained model (model.pkl) to analyze input text and provide predictions.

Features

Text Detection: Analyze text for plagiarism using a trained machine learning model. Chrome Integration: Interact with the system directly through a browser extension. Flask Backend: A lightweight REST API for model inference. Preprocessing: Automatic text preprocessing for accurate predictions. User-Friendly Interface: Simple and intuitive Chrome extension UI for entering and analyzing text.

Technologies Used

Python: For backend and model integration. Flask: To create the REST API and for deployment. NLP: Scikit-learn, NLTK, string Scikit-learn: For training and saving the model (model.pkl). Data Processing: Pandas, NumPy Chrome Extension: For a user-friendly interface. HTML/CSS/JavaScript: To build the extension UI. Vectorization: TF-IDF

Working

Backend:

The Flask server (app.py) hosts an endpoint (/detect) to receive and process text input. It loads the machine learning model from model.pkl and uses the detect(input_text) function to make predictions.

Chrome Extension:

Users enter text in the Chrome extension interface (a popup). The extension sends the input to the Flask server via a POST request. The server processes the input and returns the prediction, which is displayed in the extension.

Future Enhancements

Deploy the Flask backend to the cloud for global access. Add support for real-time plagiarism checking in text editors (e.g., Google Docs, Word). Enhance the UI for better user interaction. Add multilingual text analysis capabilities.