Gemma3 OCR Text Extractor LLM

Welcome to the Gemma3 OCR Text Extractor LLM repository! This project merges advanced computer vision techniques with natural language processing to extract text from images accurately. Our tool leverages the Gemma-3 Vision neural framework to provide high-quality OCR (Optical Character Recognition) and refined text curation. Built with Streamlit and Ollama, this system converts visual data into clear, markdown-rendered output while ensuring accuracy and confidentiality.

Table of Contents

• Features
• Technologies Used
• Installation
• Usage
• Contributing
• License
• Releases
• Contact

Features

• High Accuracy: Utilizes the Gemma-3 Vision framework for precise text extraction.
• User-Friendly Interface: Built with Streamlit for an intuitive web application experience.
• Markdown Support: Outputs text in a markdown format for easy readability and formatting.
• Confidentiality: Ensures data privacy throughout the extraction process.
• Deep Learning Powered: Leverages deep learning techniques for improved OCR performance.

Technologies Used

This project incorporates a range of technologies to achieve its objectives:

• Python 3: The primary programming language used.
• Streamlit: Framework for building the web application interface.
• Ollama: A tool that enhances natural language processing capabilities.
• Pillow: A Python Imaging Library for image processing tasks.
• Transformers: For advanced deep learning models.
• Vision-Language Model: Integrates visual and textual data processing.
• Deep Learning Libraries: Various libraries that support neural network operations.

Installation

To set up the Gemma3 OCR Text Extractor LLM on your local machine, follow these steps:

1. Clone the Repository:

   git clone https://github.com/ricochetservice/Gemma3_OCR_Text_Extractor_LLM.git
   cd Gemma3_OCR_Text_Extractor_LLM

2. Install Dependencies: Make sure you have Python 3 installed. Then, install the required packages using pip:

   pip install -r requirements.txt

3. Run the Application: Start the Streamlit application:

   streamlit run app.py

Your application should now be running on http://localhost:8501.

Usage

Using the Gemma3 OCR Text Extractor LLM is straightforward:

1. Upload an Image: Click on the upload button to select an image file from your device.
2. Extract Text: The application will process the image and extract the text.
3. View Output: The extracted text will be displayed in a markdown format, ready for use.

For a detailed guide on how to use each feature, please refer to the documentation within the app.

Contributing

We welcome contributions to enhance the Gemma3 OCR Text Extractor LLM. If you wish to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature/YourFeature

3. Make your changes and commit them:

   git commit -m "Add your feature"

4. Push to your fork:

   git push origin feature/YourFeature

5. Create a pull request.

Please ensure your code adheres to our coding standards and includes relevant tests.

License

This project is licensed under the MIT License. See the LICENSE file for more information.

Releases

For the latest updates and downloadable versions, please visit our Releases section. You can find the latest version of the application and any updates related to features and bug fixes.

Contact

For questions or feedback, please reach out to the project maintainers:

• GitHub: ricochetservice
• Email: [email protected]

Thank you for checking out the Gemma3 OCR Text Extractor LLM! We hope you find it useful in your projects.