from transformers import pipeline
from PIL import Image

# Load the pipeline
pipe = pipeline("image-to-text", model="a0a7/gregg-recognition")

# Load an image
image = Image.open("path/to/shorthand/image.png")

# Generate text
result = pipe(image)
print(result[0]['generated_text'])

Using the original package

python

from gregg_recognition import GreggRecognition

# Initialize the recognizer
recognizer = GreggRecognition(model_type="image_to_text")

# Recognize text from image
result = recognizer.recognize("path/to/image.png")
print(result)

Command Line Interface

bash

# Install the package
pip install gregg-recognition

# Use the CLI
gregg-recognize path/to/image.png --verbose

Model Performance

The model uses advanced pattern recognition techniques optimized for Gregg shorthand notation.

Training Details

Framework: PyTorch
Optimizer: Adam
Architecture: Custom CNN-LSTM with pattern database
Input Resolution: 256x256 pixels
Preprocessing: Grayscale conversion, normalization

Limitations

Optimized specifically for Gregg shorthand notation
Performance may vary with image quality
Best results with clear, high-contrast images

Citation

If you use this model in your research, please cite:

bibtex

@misc{gregg-recognition,
  title={Gregg Shorthand Recognition Model},
  author={Your Name},
  year={2025},
  url={https://huggingface.co/a0a7/gregg-recognition}
}

Contact

For questions or issues, please open an issue on the GitHub repository.