🛡️ AuthentiCheck: Deep Writing Analysis & Authorship Forensics

AuthentiCheck is a sophisticated, browser-based tool designed to analyze the entire process of text creation. It goes far beyond a simple plagiarism check by capturing and visualizing detailed user behavior, including typing speed, rhythm, correction patterns, and the distinction between typed and pasted content.

This project is currently a fully client-side application that uses localStorage for data persistence, making it a powerful standalone demo.

➡️ View Live Demo (This link will work after setting up GitHub Pages)

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✨ Key Features

• Typed vs. Pasted Analysis: Identify distinguishes between content that was typed by the user and content that was pasted from an external source.
• Sentence-by-Sentence Forensics:
  • Typing Speed (WPM)
  • Time taken to write
  • Number of corrections (backspaces, deletes)
• Interactive Dashboard: A comprehensive dashboard that aggregates all session data a:
  • Overall text composition (Typed vs. Pasted)
  • Per-sentence WPM, duration, and correction analysis
• Zero Backend Required: Runs entirely in the browser, making it easy to deploy and test.

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🛠️ Technology Stack

• Frontend: HTML5, CSS3, Vanilla JavaScript (ES6+)
• Data Visualization: Chart.js
• Data Storage: Browser localStorage

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🏛️ How It Works (Architecture)

AuthentiCheck's brilliance lies in its client-side architecture:

1. Tracking (track.html & main.js): As the user types in the editor, main.js listens for every keystroke, paste, and deletion. It logs these events with timestamps and categorizes text segments.
2. Data Persistence: All this detailed session data, including sentence-level stats, is saved into the browser's localStorage under the key studentTrackingDataAll.
3. Visualization (dashboard.html & dashboard.js): The dashboard page reads the entire history from localStorage, processes it, and uses Chart.js to render the analytics.

This self-contained model makes it perfect for demos, but would require a backend for a multi-user, production environment.

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📂 File Structure

File	Description
index.html	A marketing-style landing page describing the product's capabilities.
track.html	The core application page with the text editor where user activity is tracked.
main.js	The heart of the application. Contains all the logic for tracking typing, pasting, and sentence analysis.
dashboard.js	The script that loads data from localStorage, performs calculations,etc

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🚀 How to Run Locally

Since this project has no backend, you can run it easily with any local web server.

Method 1: Using VS Code Live Server (Easiest)

1. Open the project folder in Visual Studio Code.
2. Install the Live Server extension from the marketplace.
3. Right-click on index.html or track.html in the file explorer and select "Open with Live Server".

Method 2: Using Python

1. Make sure you have Python installed.
2. Open a terminal in the project's root directory.
3. Run the command: python -m http.server
4. Open your browser and navigate to http://localhost:8000.

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📋 How to Use

1. Navigate to track.html.
2. Start typing in the text area. You can also paste content and make corrections.
3. Observe the live statistics at the bottom of the editor.
4. When you are finished, click the "Analysis" button.
5. A modal will open showing the detailed dashboard with analytics of your writing session.
6. You can go back, type more, and re-open the analysis to see the updated data.

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🛣️ Future Improvements (Roadmap)

This project has a fantastic foundation. Here's how it could be evolved into a full-fledged SaaS product:

• Backend Integration:
  • Replace localStorage with a proper backend (e.g., Node.js/Express).
  • Use a database (like PostgreSQL or MongoDB) to store user and session data.
  • Implement user authentication to separate data from different users.
• Real-time Proctor Dashboard:
  • Use WebSockets (e.g., Socket.IO) to stream data from track.html to the dashboard in real-time, allowing a proctor to monitor multiple users simultaneously.
• Advanced Analysis:
  • Integrate Natural Language Processing (NLP) libraries to analyze the quality of the writing (e.g., grammar, tone, complexity).

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🤝 Contributing

Contributions are welcome! Please feel free to submit a pull request.

1. Fork the repository.
2. Create your feature branch (git checkout -b feature/AmazingFeature).
3. Commit your changes (git commit -m 'Add some AmazingFeature').
4. Push to the branch (git push origin feature/AmazingFeature).
5. Open a Pull Request.