SafeDocs

SafeDocs is a privacy-focused web application designed to securely analyze and extract Personally Identifiable Information (PII) from digital documents, such as PDFs and images, while ensuring data security and compliance.

Features

• Comprehensive PII Detection
  SafeDocs automatically identifies and extracts sensitive information, including:

  • Names
  • Dates of Birth
  • Aadhaar Numbers
  • PAN Numbers
  • Addresses
  • Virtual ID (VID)

• Enhanced Security Measures
  SafeDocs supports password-protected files, ensuring that sensitive data is analyzed securely without compromising user privacy.

• Accurate Data Extraction and Formatting
  With advanced OCR (Optical Character Recognition) and custom regular expressions, SafeDocs cleans and validates extracted data to match specific Indian ID formats (e.g., Aadhaar, VID) and structured addresses.

• Streamlined Workflow for Compliance
  SafeDocs offers a user-friendly interface, ideal for both individuals and organizations aiming to manage PII securely, supporting privacy compliance and auditing tasks.

Tech Stack

• Frontend: React
• Backend: Python (Flask)
• Database: MongoDB
• OCR: pytesseract with preprocessing for enhanced accuracy

How It Works

1. Upload Document: SafeDocs accepts PDFs and images, including those with complex formats or images embedded.
2. OCR and Extraction: Text is extracted using OCR, and PII is identified based on predefined patterns and formats.
3. Post-Processing: Extracted text is processed to clean and validate PII fields (e.g., Aadhaar, VID, addresses).
4. Results: Extracted PII is displayed in a secure, structured format, allowing users to review and manage data effectively.

Prerequisites

Before running the project, make sure you have the following installed:

• Python (version 3.6 or higher)
• Django (version 3.0 or higher)

Installation

1. Clone the project repository to your local machine. git clone https://github.com/AnkurGarg07/DocSafeSIH.git
2. Open a terminal or command prompt and navigate to the project directory.
3. Create a virtual environment by running the command: python -m venv .venv
4. Activate the virtual environment:
   • On Windows: .venv\Scripts\activate
   • On macOS/Linux: source .venv/bin/activate
5. Install the project dependencies by running: pip install -r requirements.txt

Running the Project

1. Run the following command to apply the database migrations: python manage.py migrate
2. Start the development server by running: python manage.py runserver
3. Open your web browser and navigate to http://localhost:8000 to access the project.

Additional Notes

• If you encounter any issues during the installation or running of the project, refer to the project documentation or seek help from the project contributors.