a0a7/gregg-recognition

Gregg Shorthand Recognition Model

This model recognizes Gregg shorthand notation from images and converts it to readable text.

Model Description

• Model Type: Image-to-Text recognition
• Architecture: CNN-LSTM with advanced pattern recognition
• Training Data: Gregg shorthand samples
• Language: English
• License: MIT

Intended Use

This model is designed to:

• Recognize Gregg shorthand from scanned documents
• Convert historical stenographic notes to digital text
• Assist in digitizing shorthand archives
• Support stenography education and research

How to Use

Using the Hugging Face Transformers library

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

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

# 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:

@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.