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🏷️ GovID Extractor

📖 Overview

GovID Extractor is a powerful web application designed to extract crucial information from government-issued documents such as passports and driving licenses. Using OCR (Optical Character Recognition) technology powered by Tesseract.js, the application enables users to upload images of their documents and automatically retrieve key details like name, document number, and expiration date. The intuitive user interface ensures a seamless experience for document handling and data extraction.

🚀 Key Features

  • Effortless Document Upload: Upload images of government-issued IDs and documents.
  • Automatic OCR Processing: Leverages Tesseract.js for fast and accurate text extraction.
  • Document Type Selection: Choose between various document types (e.g., Passport, Driving License) for targeted extraction.
  • Data Extraction: Quickly extract key information:
    • 📛 Name
    • 🆔 Document Number
    • 📅 Expiration Date

🖥️ Tech Stack

This project utilizes a range of technologies to ensure scalability, speed, and reliability:

  • Frontend: React.js for dynamic and responsive UI.
  • Backend: Node.js with Express.js for building the API and handling server-side logic.
  • OCR Engine: Tesseract.js for text recognition from images.
  • File Upload: Multer for handling file uploads.
  • Data Storage: JSON for storing extracted data temporarily.

🌐 Getting Started

📦 Prerequisites

Make sure you have the following installed:

  • Node.js (v18 or higher)
  • npm (Node Package Manager)

🔧 Installation Steps

  1. Clone the Repository:

    git clone https://github.com/qaidjoharj53/OCR-Government-ID.git
cd OCR-Government-ID

  2. Install Dependencies:

    Install the necessary backend and frontend dependencies:

    cd server
npm install

  3. Start the Backend Server:

    Run the backend server:

    npm start

    The server will be available at http://localhost:5000.

  4. Run the Frontend:

    In a separate terminal, navigate to the frontend directory and start the React app:

    npm start

    The frontend will be available at http://localhost:3000.

📁 Project Structure

Here is an overview of the project directory:

├── client/
├── server/
├── .gitignore
├── LICENSE
└── README.md

🎨 UI Components

The web app features simple yet effective UI elements:

  • File Upload Button: To upload documents.
  • Document Type Dropdown: Allows users to select the type of document (e.g., Passport, Driving License).
  • OCR Results Display: A card that showcases the extracted details (name, document number, expiration date).

📄 Usage

  1. Select Document Type: Choose the type of document you're uploading (e.g., Driving License or Passport).
  2. Upload Document: Click on the "Upload" button to select your file.
  3. View Extracted Data: The OCR engine will process the document, and the extracted information will be displayed on the screen.

📚 Example Output

For example, after uploading a driving license, the extracted data might look like:

{
	"name": "Name Surname",
	"documentNumber": "MH17 20160001642",
	"expirationDate": "11-01-2036"
}

🛠️ Future Enhancements

  • Additional Document Types: Support for more document types like national ID cards, voter ID, etc.
  • Improved OCR Accuracy: Implement custom-trained data for more precise OCR results.
  • User Authentication: Allow users to save and access their scanned documents securely.
  • Mobile Support: Build a mobile-friendly version for better accessibility.

💬 Contributing

We welcome contributions from developers who want to improve this project! To contribute:

1. Fork the repository.
2. Create a new branch for your changes.
3. Make your changes and commit them.
4. Push your changes to your branch.
5. Open a pull request to the main branch.
6. Wait for the code review and merge.
7. Once merged, your changes will be live in the main branch.

Make sure to follow best coding practices and include tests for any new features or bug fixes.

Contact Us

For any questions, suggestions, or issues, feel free to reach out to Qaidjohar Jukker.

📄 License

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