Real-Time OCR Processing Platform

This project is a web application that allows users to upload images and extract text from those images using Optical Character Recognition (OCR). It leverages FastAPI for the backend and includes real-time notifications via WebSocket to inform users about the upload and processing status.

Features

• Image Upload: Users can upload image files through a simple web interface.
• OCR Processing: The application uses Tesseract to extract text from uploaded images.
• Real-Time Notifications: Users receive real-time updates about the upload and processing status.
• Extracted Text Display: The extracted text is displayed on the webpage once processing is complete.

Technologies Used

• Backend: FastAPI
• OCR Library: Tesseract
• Frontend: HTML, CSS, JavaScript
• Database: PostgreSQL (optional for extended functionality)
• WebSocket: For real-time notifications
• Containerization: Docker

Prerequisites

• Docker
• Docker Compose
• (Optional) PostgreSQL installed on your machine if you plan to use it directly instead of in Docker.

Installation

Using Docker

1. Clone the repository:

   git clone https://github.com/ankikumar1018/fastapi-ocr-webapp.git
   cd <repository-directory>
   

2. Build and run the Docker containers:

   docker-compose up --build
   

3. Access the application: Open your web browser and go to http://127.0.0.1:8000/.

Without Docker (optional)

1. Clone the repository:

   git clone <repository-url>
   cd <repository-directory>
   

2. Create a virtual environment::

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`
   

3. Install required packages:

   pip install fastapi uvicorn python-multipart aiofiles pytesseract pillow psycopg2-binary websockets
   

4. Install Tesseract:

   git clone <repository-url>
   cd <repository-directory>
   

5. Install Tesseract:

   • Follow the instructions for your operating system to install Tesseract.
   • Ensure Tesseract is added to your system's PATH.

Running the Application

1. Start the FastAPI server:

   uvicorn app:app --reload

2. Access the application: Open your web browser and go to http://127.0.0.1:8000/.

Usage

1. Use the upload form to select an image file and click the "Upload" button.
2. Watch for real-time notifications that indicate the status of your upload and OCR processing.
3. After processing is complete, The extracted text will be displayed below the upload form.

Optional: PostgreSQL Setup

To enable PostgreSQL storage for extracted data:

1. Install PostgreSQL on your machine.
2. Create a database and configure your connection in the FastAPI application (if implemented).
3. Modify the code to store the extracted text and metadata in the database.

License

This project is licensed under the MIT License.

Acknowledgments

• FastAPI
• Tesseract
• WebSocket

Contributing

Contributions are welcome! Feel free to submit a pull request or open an issue for improvements or bugs.