Tag Keeper

Overview

Tag Keeper is an AI-powered application that allows users to capture and process garment tags before cutting them off. By leveraging computer vision and OCR, the app extracts care instructions from clothing labels and retrieves detailed washing and maintenance guidance via external APIs like Laundry Buddy. The goal is to provide an effortless and scalable way to manage garment care information while enabling future smart wardrobe extensions.

Features

• Tag Recognition: Uses OCR and computer vision to extract care instructions from clothing tags.
• Garment Identification: Optionally detects the garment type using a trained model.
• API Integration: Connects with external laundry services for additional care recommendations.
• Image Processing: Implements OpenCV-based preprocessing and optional tag segmentation.
• Local & Cloud Storage: Saves processed images and extracted text on the device or cloud.
• User-Friendly Interface: Provides a clean and intuitive UI for both mobile and web users.

Installation

Backend (Flask API)

1. Clone the repository

2. Set up a virtual environment:

   python3 -m venv venv
   source venv/bin/activate  # On Windows use: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Run the Flask server:

   python app.py

   The API will be available at http://127.0.0.1:5000/

Frontend (Web Interface)

1. Navigate to the frontend directory:

   cd frontend
2. Install dependencies:

   npm install
3. Start the development server:

   npm start
   The web app will be available at http://localhost:3000/

Mobile App (React Native)

1. Install dependencies:

   npm install -g expo-cli
2. Run the application:

   expo start
   Use the Expo Go app to test it on a mobile device.

Usage

1. Upload an Image: Select a picture of a clothing tag (or both the garment and tag together).
2. Image Processing & OCR: The system detects the tag, extracts the text, and removes noise for improved accuracy.
3. API Call: The extracted text is sent to an external API (e.g., Laundry Buddy) for care instructions.
4. View Results: Users receive detailed washing and care instructions in the app.

API Endpoints

• POST /upload: Upload an image for processing.
  • Request: Multipart form-data with an image file.
  • Response: JSON containing extracted text and API results.

Future Enhancements

• Enhanced Tag Detection: Improve accuracy using deep learning models like YOLO.
• Garment Type Classification: Automatically identify clothing type for better care instructions.
• Personalized Laundry Recommendations: Store user preferences and suggest care routines.
• Cloud Sync & Multi-Device Support: Save and access tag data across devices.
• Smart Wardrobe Expansion: Integrate with inventory apps for complete clothing management.

License

This project is licensed under the MIT License.

Contributing

Contributions are welcome! Feel free to open issues or submit pull requests.

Contact

For inquiries or support, reach out