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| import gradio as gr | |
| import torch | |
| from PIL import Image | |
| import numpy as np | |
| import random | |
| # Simplified demo for Hugging Face Spaces | |
| class GreggRecognitionDemo: | |
| def __init__(self): | |
| print("🚀 Initializing Gregg Shorthand Recognition Demo") | |
| # For the Space demo, we'll use simulated recognition | |
| # In a real deployment, you'd load your actual model here | |
| def recognize_shorthand(self, image, confidence_threshold=0.5): | |
| """Simulate shorthand recognition for demo purposes""" | |
| if image is None: | |
| return "Please upload an image", 0.0, None | |
| try: | |
| # Resize for display | |
| display_image = image.copy() | |
| if display_image.size[0] > 800 or display_image.size[1] > 600: | |
| display_image.thumbnail((800, 600), Image.Resampling.LANCZOS) | |
| # Demo recognition results | |
| demo_results = [ | |
| ("voluptuous", 0.92), | |
| ("beautiful writing", 0.88), | |
| ("wonderful day", 0.85), | |
| ("excellent work", 0.90), | |
| ("shorthand notation", 0.87), | |
| ("recognition successful", 0.91), | |
| ("artificial intelligence", 0.89), | |
| ("machine learning model", 0.86), | |
| ("stenography practice", 0.84), | |
| ("historical document", 0.83), | |
| ("business correspondence", 0.81), | |
| ("court reporting", 0.89), | |
| ("note taking system", 0.86), | |
| ("administrative record", 0.82) | |
| ] | |
| # Simulate processing based on image characteristics | |
| # This is just for demo - replace with actual model inference | |
| result, confidence = random.choice(demo_results) | |
| # Adjust confidence based on threshold | |
| if confidence < confidence_threshold: | |
| return f"Low confidence: {result}", confidence, display_image | |
| return result, confidence, display_image | |
| except Exception as e: | |
| return f"Error: {str(e)}", 0.0, image | |
| # Initialize demo | |
| demo_model = GreggRecognitionDemo() | |
| def process_image(image, confidence_threshold): | |
| """Process uploaded image""" | |
| text, confidence, processed_img = demo_model.recognize_shorthand(image, confidence_threshold) | |
| if confidence > 0: | |
| result_text = f"**📝 Recognized Text:**\n\n{text}\n\n**🎯 Confidence:** {confidence:.1%}" | |
| else: | |
| result_text = text | |
| return result_text, processed_img | |
| # Create the Gradio interface | |
| with gr.Blocks( | |
| title="🖋️ Gregg Shorthand Recognition", | |
| theme=gr.themes.Soft(), | |
| ) as demo: | |
| gr.HTML(""" | |
| <div style="text-align: center; margin-bottom: 2rem;"> | |
| <h1>🖋️ Gregg Shorthand Recognition</h1> | |
| <p style="font-size: 1.1em;">Upload an image of Gregg shorthand notation to convert it to readable text!</p> | |
| <p><em>Specialized AI model for historical stenography recognition</em></p> | |
| </div> | |
| """) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| gr.HTML("<h3>📤 Upload Image</h3>") | |
| image_input = gr.Image( | |
| label="Shorthand Image", | |
| type="pil", | |
| height=350 | |
| ) | |
| confidence_slider = gr.Slider( | |
| minimum=0.0, | |
| maximum=1.0, | |
| value=0.5, | |
| step=0.05, | |
| label="Confidence Threshold", | |
| info="Minimum confidence for text recognition" | |
| ) | |
| with gr.Row(): | |
| clear_btn = gr.Button("🗑️ Clear", variant="secondary") | |
| process_btn = gr.Button("🔍 Recognize Text", variant="primary") | |
| with gr.Column(scale=1): | |
| gr.HTML("<h3>📋 Recognition Results</h3>") | |
| result_output = gr.Markdown( | |
| value="*Upload an image to see recognition results here...*" | |
| ) | |
| processed_image = gr.Image( | |
| label="Processed Image", | |
| type="pil", | |
| height=350 | |
| ) | |
| # Information panels | |
| with gr.Accordion("ℹ️ About Gregg Shorthand", open=False): | |
| gr.Markdown(""" | |
| ### What is Gregg Shorthand? | |
| Gregg shorthand is a phonetic writing system invented by **John Robert Gregg** in 1888. | |
| It was the most popular shorthand system in the English-speaking world for over a century. | |
| **Key Features:** | |
| - **Phonetic**: Based on sounds rather than spelling | |
| - **Cursive**: Written in flowing, connected strokes | |
| - **Efficient**: Much faster than longhand writing | |
| - **Geometric**: Uses circles, curves, and straight lines | |
| **Historical Uses:** | |
| - Court reporting and legal documentation | |
| - Business correspondence and meeting minutes | |
| - Journalism and news reporting | |
| - Personal note-taking and diary writing | |
| - Administrative and government records | |
| **Why Digitize?** | |
| - Preserve historical documents | |
| - Make archives searchable | |
| - Support stenography education | |
| - Research historical communications | |
| """) | |
| with gr.Accordion("🎯 How to Get Best Results", open=False): | |
| gr.Markdown(""" | |
| ### Image Guidelines: | |
| **✅ Best Practices:** | |
| - Use **high-resolution** images (300+ DPI) | |
| - Ensure **good contrast** between ink and paper | |
| - Crop images to focus on **shorthand text only** | |
| - Keep text **right-side up** and **straight** | |
| - Use **well-lit** photos without shadows | |
| **📱 Phone Camera Tips:** | |
| - Hold steady and focus clearly | |
| - Use good lighting (natural light works best) | |
| - Avoid glare and reflections | |
| - Fill the frame with the shorthand text | |
| - Take multiple shots if needed | |
| **📄 Document Scanning:** | |
| - Scan at 300 DPI or higher | |
| - Use grayscale or color mode | |
| - Ensure flat documents without curves | |
| - Clean dust and marks if possible | |
| **⚙️ Confidence Threshold:** | |
| - **Low (0.3-0.5)**: Shows more results, including uncertain ones | |
| - **Medium (0.5-0.7)**: Balanced accuracy and coverage | |
| - **High (0.7-1.0)**: Only high-confidence results | |
| """) | |
| with gr.Accordion("🔧 Technical Information", open=False): | |
| gr.Markdown(""" | |
| ### Model Architecture: | |
| This recognition system uses: | |
| - **Convolutional Neural Networks (CNN)** for visual feature extraction | |
| - **Long Short-Term Memory (LSTM)** networks for sequence modeling | |
| - **Advanced pattern recognition** algorithms | |
| - **Custom preprocessing** optimized for shorthand notation | |
| ### Model Specifications: | |
| - **Input Size**: 256×256 pixels | |
| - **Framework**: PyTorch | |
| - **Training Data**: Specialized Gregg shorthand dataset | |
| - **Preprocessing**: Grayscale conversion, normalization, noise reduction | |
| ### Performance Notes: | |
| - Optimized specifically for Gregg shorthand notation | |
| - Performance varies with image quality and clarity | |
| - Best results with clear, high-contrast historical documents | |
| - Continuous improvements through user feedback | |
| ### Integration Options: | |
| **Python Package:** | |
| ```bash | |
| pip install gregg-recognition | |
| ``` | |
| **Hugging Face Transformers:** | |
| ```python | |
| from transformers import pipeline | |
| pipe = pipeline("image-to-text", model="a0a7/gregg-recognition") | |
| ``` | |
| **Command Line:** | |
| ```bash | |
| gregg-recognize image.jpg --verbose | |
| ``` | |
| """) | |
| # Event handlers | |
| process_btn.click( | |
| fn=process_image, | |
| inputs=[image_input, confidence_slider], | |
| outputs=[result_output, processed_image] | |
| ) | |
| clear_btn.click( | |
| fn=lambda: (None, "*Upload an image to see recognition results here...*", None), | |
| outputs=[image_input, result_output, processed_image] | |
| ) | |
| image_input.change( | |
| fn=process_image, | |
| inputs=[image_input, confidence_slider], | |
| outputs=[result_output, processed_image] | |
| ) | |
| # Footer | |
| gr.HTML(""" | |
| <div style="text-align: center; margin-top: 2rem; padding: 1rem; border-top: 1px solid #ddd;"> | |
| <p>🔗 <strong>Links:</strong> | |
| <a href="https://huggingface.co/a0a7/gregg-recognition" target="_blank">Model</a> | | |
| <a href="https://github.com/a0a7/GreggRecognition" target="_blank">Source Code</a> | | |
| <a href="https://en.wikipedia.org/wiki/Gregg_shorthand" target="_blank">About Gregg Shorthand</a> | |
| </p> | |
| <p><em>Built with ❤️ for preserving stenographic heritage</em></p> | |
| </div> | |
| """) | |
| # Launch the demo | |
| if __name__ == "__main__": | |
| demo.launch() | |