Python Project

A Python project to extract the content of input images and translate the detected text into a requested language. Now featuring a web-based GUI for easier interaction.

Project Structure

.
├── src/              # Source code
├── tests/            # Test files
├── app.py            # Streamlit Web Application (GUI)
├── .gitignore        # Git ignore patterns
├── pyproject.toml    # Project configuration
└── README.md         # This file

Setup

1. Create a virtual environment:

   python -m venv venv

2. Activate the virtual environment:

   • Windows: venv\Scripts\activate
   • Unix/MacOS: source venv/bin/activate

3. Install dependencies:

   # Install core project and CLI dependencies
   pip install -e .
   # Install additional UI and Image Processing dependencies
   pip install streamlit opencv-python pillow deep-translator easyocr

Usage

Run the main application:

1. Web Application (Recommended)

   The project now includes a Web UI for a more interactive experience, featuring OpenCV image cleaning and a language dropdown.

   Run the Streamlit app:

   streamlit run app.py

Features: - Drag-and-drop image uploading.

OpenCV Pre-processing: Toggle "OpenCV Clean" to improve OCR accuracy on noisy images.

Language Selection: Choose from Hindi, Spanish, French, Japanese, Arabic, or English.

Download Result: Export your translations directly as a .txt file.

2. Command Line Interface (CLI)

   You can still run the script directly from the terminal.

Extract text only (English):

python src/main.py <image_path>

Extract and Translate:

python src/main.py <image_path> <hi/es/fr/ja/ar/en>

Testing

The project includes unit tests for each image processing scenario. Each test file validates text extraction for specific images.

Available Test Files

• test_door_image.py - Tests extraction of "What are the Best Colours for a Plain Door?" from DoorImage.jpeg
• test_door_image_translation_de.py - Tests extraction and German translation from DoorImage.jpeg
• test_nike_image.py - Tests Nike brand text extraction from Nike.jfif
• test_mcd_image.py - Tests McDonald's text extraction from McD.jfif
• test_5star_image.py - Tests 5 Star chocolate text extraction from 5Star.jfif
• test_india_sizes_image.py - Tests India size chart text extraction from IndiaSizes.jfif

Running Tests

Run all tests:

pytest tests/ -v

Run a specific test file:

pytest tests/test_door_image.py -v
pytest tests/test_door_image_translation_de.py -v
pytest tests/test_nike_image.py -v
pytest tests/test_mcd_image.py -v
pytest tests/test_5star_image.py -v
pytest tests/test_india_sizes_image.py -v

Run tests with coverage report:

pytest tests/ --cov=src --cov-report=term-missing

Run a specific test function:

pytest tests/test_door_image.py::test_door_image_extraction -v

Test Requirements

Install test dependencies:

pip install pytest pytest-cov

Note: Tests use mocked EasyOCR responses to avoid actual image processing during testing, making them fast and reliable.

Development

This project follows Python best practices:

• PEP 8 style guide
• Type hints where appropriate
• Comprehensive unit tests
• Clear documentation
• Uses OpenCV for Gaussian Thresholding to handle varied lighting in images.

License

This project is licensed under the Apache License, Version 2.0 - see the LICENSE.md file for details

Happy Contributing