frkhan/bangla-ocr

🇧🇩 Bangla OCR Web App with Gradio & EasyOCR

This project is a lightweight Optical Character Recognition (OCR) web application built using Gradio and EasyOCR. It allows users to upload images containing Bangla or English text and extract it instantly through a simple browser interface. Designed for fast deployment and minimal setup, it runs locally, in Docker, or via Docker Compose.

🚀 Features

• —📷 Upload or drag-and-drop images for OCR.
• —🔍 Extract both Bangla and English text using EasyOCR.
• —⚡ GPU-accelerated for fast inference (if a compatible GPU and CUDA are available).
• —🌐 Launch a user-friendly web interface with Gradio.
• —🐳 Full support for containerized deployment with Docker and Docker Compose.
• —🧼 Clean UI with dual output: extracted text and processing duration.

🔗 Live Demo

• —Live App: Try it out here: [Bangla OCR Demo App](https://huggingface.co/spaces/frkhan/bangla-ocr)
• —Project Story: Curious about how this app was built? [Read the full story on Medium](https://frkhan.medium.com/turning-pages-into-pixels-the-making-of-a-bangla-ocr-app-9022bbffcd60) to see the journey from idea to deployment.

🧰 Tech Stack

📦 Installation

You can run the app in three different ways:

🔧 Option 1: Local Setup

1. 1.Clone the repository:

    git clone https://github.com/KI-IAN/bangla-ocr-app
    cd bangla-ocr-app

1. 1.Create and activate a virtual environment: A virtual environment isolates the project's dependencies from your system's global Python packages.

    # Create the virtual environment (you only need to do this once per project)
    # On some systems, you might need to use `python3` instead of `python`
    python -m venv venv

Next, activate the environment. The command depends on your operating system:

• —On Windows (Command Prompt / PowerShell):

        venv\Scripts\activate

• —On macOS / Linux (bash, zsh, etc.):

        source venv/bin/activate

Your terminal prompt should now change to show `(venv)` at the beginning.

1. 1.Install dependencies: With the virtual environment active, install the required packages.

    pip install -r requirements.txt

1. 1.Launch the app:

    python app.py

The app will be running at http://127.0.0.1:12300.

(When you're finished, you can leave the virtual environment by simply running the `deactivate` command.)

🐳 Option 2: Docker

1. 1.Build the Docker image:

    docker build -t bangla-ocr-app .

1. 1.Run the container:

    docker run -p 12300:7860 bangla-ocr-app

Open your browser and visit: http://localhost:12300

🧱 Option 3: Docker Compose

# To Run in Live environment. It automatically uses the docker-compose.yml
docker-compose up --build 

# Or If you use the latest docker compose command, use the following

docker compose up --build

Access the app at http://localhost:12300

# To Run in local environment use docker-compose.dev.yml if you want to reflect your code changes without rebuilding docker container
docker-compose -f docker-compose.dev.yml up --build

# Or If you use the latest docker compose command, use the following
docker compose -f docker-compose.dev.yml up --build

Access the app at http://localhost:12300

🖼️ Example

Upload an image containing Bangla or English text.

Example Input:

(Note: Replace with a link to an actual example image.)

Example Output:

Extracted Text:
"বাংলা ভাষা আমাদের গর্ব।"

⏱️ Time taken: 0.87 seconds

📁 File Structure

bangla-ocr-app/
├── app.py                  # Main application logic
├── requirements.txt        # Python dependencies
├── Dockerfile              # Container setup
├── docker-compose.yml      # Multi-container orchestration for production
├── docker-compose.dev.yml  # Multi-container orchestration for development
├── .env                    # Environment variables for Langfuse (Optional)
└── README.md               # Project documentation

🌍 Language Support

EasyOCR supports over 80 languages. This app is pre-configured for:

• —🇧🇩 Bangla (bn)
• —🇺🇸 English (en)

To add more languages, modify the following line in app.py:

# From
reader = easyocr.Reader(['bn', 'en'], gpu=True)

# To (for example, adding Hindi, Arabic, Urdu, Malay, Chinese, and Japanese)
reader = easyocr.Reader(['bn', 'en', 'hi', 'ar', 'ur', 'ms', 'ch_sim', 'ja'], gpu=True)

📜 License

This project is open-source and distributed under the [MIT License](https://opensource.org/licenses/MIT). Feel free to use, modify, and distribute it with attribution.

🤝 Acknowledgements

• —[EasyOCR](https://github.com/JaidedAI/EasyOCR) for its powerful and accessible multilingual OCR library.
• —[Gradio](https://www.gradio.app/) for making it incredibly simple to create machine learning interfaces.
• —[PyTorch](https://pytorch.org/) for powering the deep learning backend.
• —[Docker](https://www.docker.com) — Containerization platform for reproducible deployment across environments.
• —[Hugging Face Spaces](https://huggingface.co/spaces) — Free hosting platform for ML demos with secret management and GPU support.
• —[Langfuse](https://langfuse.com/) for providing excellent observability tools.

“Small tools, big impact.” — Let’s make machine learning approachable, one project at a time.