README.md

metadata

title: Image Text Extractor
emoji: 📄
colorFrom: blue
colorTo: indigo
sdk: docker
sdk_version: 1.28.0
app_file: streamlit_app.py
pinned: false
license: mit

Image Text Extractor

This project is a Streamlit application that uses the olmOCR model (based on Qwen2.5-VL) to extract text from images. It provides a user-friendly interface to upload images and view the extracted text along with metadata.

Features

• Image Upload: Support for PNG, JPG, and JPEG formats.
• Text Extraction: Uses state-of-the-art Vision-Language Models for accurate OCR.
• Metadata Extraction: Extracts additional information like primary language, rotation, and content type (table, diagram).
• JSON Export: Download extraction results as JSON files.
• Configurable: Adjust maximum token generation for longer documents.

Installation

1. Clone the repository:

   git clone <repository-url>
   cd image-text-extractor
   

2. Create a virtual environment (recommended):

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

3. Install dependencies:

   pip install -r requirements.txt
   

Usage

1. Run the Streamlit app:

   streamlit run streamlit_app.py
   

2. Open your browser: The app should automatically open in your default browser at http://localhost:8501.

Testing

This project uses pytest for unit testing.

1. Run tests:

   pytest tests/
   

Project Structure

• streamlit_app.py: The main entry point for the Streamlit application.
• service/: Contains the backend logic for text extraction.
  • text_extraction_service.py: The core service class handling model interaction.
• tests/: Unit tests for the application.
• requirements.txt: Python dependencies.

License

[Add License Here]