{"id":24810570,"url":"https://github.com/kumarmhaske/ocr-flask-application","last_synced_at":"2025-03-25T13:22:45.194Z","repository":{"id":270504312,"uuid":"910580312","full_name":"KumarMhaske/OCR-Flask-Application","owner":"KumarMhaske","description":"OCR Flask Application: A web application built using Flask that leverages Azure Computer Vision API for Optical Character Recognition (OCR). Users can upload images, extract text from them, and download the processed results. 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The application allows users to upload images, extract text, and download the results. It's deployed on **Azure App Service** for easy access and scalability.\n\n## Features\n\n- Upload images through a simple web interface\n- Extract text from images using Azure Computer Vision OCR API\n- Download the uploaded image after processing\n- Easy to use and lightweight Flask app\n- Hosted on Azure App Service\n\n## Technologies Used\n\n- **Flask**: Web framework for building the application\n- **Azure Computer Vision API**: Used for performing OCR on uploaded images\n- **HTML/CSS**: For creating the front-end interface\n- **JavaScript**: To handle image uploads\n- **Python**: Backend programming language\n- **Pillow**: Python Imaging Library for image handling\n- **GitHub Actions**: Continuous integration and deployment\n\n## Requirements\n\n- Python 3.6+\n- Flask\n- Azure SDK (`azure-cognitiveservices-vision`)\n- Pillow\n\n## Installation\n\nTo run this application locally:\n\n1. Clone the repository:\n\n   ```bash\n   git clone https://github.com/your-github-username/OCR-Flask-Application.git\n   ```\n\n2. Navigate into the project directory:\n\n   ```bash\n   cd OCR-Flask-Application\n   ```\n\n3. Create a virtual environment:\n\n   ```bash\n   python -m venv venv\n   ```\n\n4. Activate the virtual environment:\n\n   - On Windows:\n\n     ```bash\n     .\\venv\\Scripts\\activate\n     ```\n\n   - On macOS/Linux:\n\n     ```bash\n     source venv/bin/activate\n     ```\n\n5. Install the required dependencies:\n\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n6. Set up your Azure Computer Vision API key and endpoint as environment variables:\n\n   ```bash\n   export COMPUTER_VISION_KEY=\"your_key\"\n   export COMPUTER_VISION_ENDPOINT=\"your_endpoint\"\n   ```\n\n   On Windows, use `set` instead of `export`.\n\n7. Run the application:\n\n   ```bash\n   python app.py\n   ```\n\n8. Access the app in your browser at `http://127.0.0.1:5000`.\n\n## Deployment\n\nThe application is deployed on **Azure App Service**. You can access it live through the following link:\n\n[**Live Application**](https://ocrflaskapp-dxebc7d3dfh8hvha.eastus-01.azurewebsites.net/)\n\nhttps://ocrflaskapp-dxebc7d3dfh8hvha.eastus-01.azurewebsites.net/\n\nIf you want to deploy the app, follow these steps:\n\n1. Push the code to your GitHub repository.\n2. Create a new App Service on Azure.\n3. Configure continuous deployment from GitHub in the App Service settings.\n\n## Screenshots\n\n### Home Page\n\n![Home Page](https://github.com/KumarMhaske/OCR-Flask-Application/blob/a1375d5a0f1c7f1abd68958d08cdb4cf1cc62784/Homepage%201.PNG)\n\n### OCR Result\n\n![OCR Result](https://github.com/KumarMhaske/OCR-Flask-Application/blob/a1375d5a0f1c7f1abd68958d08cdb4cf1cc62784/result%202.PNG)\n\n## Usage\n\n1. Open the app in your browser.\n2. Upload an image containing text.\n3. The extracted text will be displayed on the results page.\n4. You can download the image as well.\n\n## Contributing\n\nFeel free to fork the repository and submit pull requests. Contributions are welcome!\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkumarmhaske%2Focr-flask-application","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkumarmhaske%2Focr-flask-application","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkumarmhaske%2Focr-flask-application/lists"}