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---
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**:
```bash
git clone <repository-url>
cd image-text-extractor
```
2. **Create a virtual environment** (recommended):
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. **Install dependencies**:
```bash
pip install -r requirements.txt
```
## Usage
1. **Run the Streamlit app**:
```bash
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**:
```bash
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]
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