Update README.md

README.md

@@ -1,110 +1,114 @@
----
-license: mit
-language:
-- en
-pipeline_tag: image-to-text
-tags:
-- gregg-shorthand
-- handwriting-recognition
-- ocr
-- historical-documents
-- stenography
-library_name: pytorch
----
-
-# 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
-
-```python
-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](https://github.com/a0a7/GreggRecognition).
+---
+license: mit
+language:
+- en
+pipeline_tag: image-to-text
+tags:
+- gregg-shorthand
+- handwriting-recognition
+- ocr
+- historical-documents
+- stenography
+library_name: pytorch
+datasets:
+- a0a7/Gregg-1916
+metrics:
+- accuracy
+---
+
+# 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
+
+```python
+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](https://github.com/a0a7/GreggRecognition).