signlib 1.0.1

AI Signature Generator for Synthetic Data - Automatically add signatures to documents for ML training datasets

SignLib - AI Signature Generator for Synthetic Data

SignLib is a Python library for automatically adding signatures to documents, designed specifically for synthetic data generation and AI/ML training datasets. It intelligently removes backgrounds, adapts colors, and positions signatures naturally on documents.

🎯 Use Cases

• AI Training Data: Generate synthetic signed documents for machine learning models
• Dataset Augmentation: Create diverse training samples with varied signature positions and styles
• Document Automation: Batch process documents with automated signature placement
• Testing & Development: Generate test documents without manual signing

✨ Features

✅ Automatic Background Removal - Removes signature background automatically
✅ Smart Positioning - Finds optimal white space in document (customizable)
✅ Color Adaptation - Auto-detects text color from document or use custom colors
✅ Auto-Scaling - Scales signature based on document size
✅ Rotation Support - Optional signature rotation for natural appearance
✅ Multiple Formats - Supports PDF, TIFF, PNG, JPEG
✅ Position Control - Customize signature placement with bottom_percent and right_percent

📦 Installation

pip install signlib

🚀 Quick Start

Basic Usage

from signlib import create_sign

# Simplest usage - auto-detect signature color
create_sign('document.pdf', 'signature.png')

# Specify output path
create_sign('document.pdf', 'signature.png', output_path='signed_document.pdf')

Custom Color

from signlib import create_sign

# Blue signature
create_sign('document.pdf', 'signature.png', signature_color=(0, 0, 255))

# Black signature
create_sign('document.pdf', 'signature.png', signature_color=(0, 0, 0))

# Dark gray signature
create_sign('document.pdf', 'signature.png', signature_color=(50, 50, 50))

Position Control (New!)

from signlib import create_sign

# Search in bottom 40% and right 40% of document
create_sign(
    'document.pdf',
    'signature.png',
    bottom_percent=40,  # Search from bottom 40% upward
    right_percent=40    # Search from right 40% leftward
)

# Place signature in bottom-left area
create_sign(
    'document.pdf',
    'signature.png',
    bottom_percent=30,  # Bottom 30%
    right_percent=70    # Left 70% (starting from right)
)

Advanced Usage

from signlib import create_sign

# Full control over all parameters
create_sign(
    document_path='document.tif',
    sign_path='signature.png',
    output_path='signed.tif',
    signature_color=(0, 0, 100),  # Dark blue
    scale_factor=0.15,             # 15% of document width
    rotation_angle=5.0,            # 5 degrees clockwise
    bottom_percent=25,             # Bottom 25%
    right_percent=50               # Right 50%
)

Batch Processing for AI Training

from signlib import create_sign
from pathlib import Path

# Create synthetic training data
doc_folder = Path('documents/')
signature_folder = Path('signatures/')
output_folder = Path('synthetic_data/')
output_folder.mkdir(exist_ok=True)

# Generate diverse signed documents
for doc_file in doc_folder.glob('*.pdf'):
    for sig_file in signature_folder.glob('*.png'):
        output_name = f"{doc_file.stem}_{sig_file.stem}_signed.pdf"
        output_path = output_folder / output_name
        
        create_sign(
            str(doc_file),
            str(sig_file),
            output_path=str(output_path),
            scale_factor=0.12,         # Vary these for diversity
            rotation_angle=0.0,
            bottom_percent=25,
            right_percent=50
        )
        print(f"✓ Generated: {output_name}")

Class-Based Usage (Advanced)

from signlib import SignatureProcessor

processor = SignatureProcessor()

# Step-by-step processing
result = processor.create_sign(
    document_path='document.pdf',
    sign_path='signature.png',
    signature_color=None,      # Auto-detect
    scale_factor=0.12,
    rotation_angle=0.0,
    enhance_contrast=True,
    bottom_percent=25,
    right_percent=50
)

print(f"Signed document: {result}")

📖 API Reference

create_sign() Function

Parameter	Type	Default	Description
document_path	str	Required	Path to document file
sign_path	str	Required	Path to signature file
output_path	str	None	Output file path (auto-generated if None)
signature_color	tuple	None	RGB color (r, g, b). None for auto-detect
scale_factor	float	0.12	Signature size ratio (0.12 = 12% of document width)
rotation_angle	float	0.0	Rotation angle in degrees
bottom_percent	float	25	Search area from bottom (25 = bottom 25%)
right_percent	float	50	Search area from right (50 = right 50%)

Position Control

• bottom_percent: Controls how far from the bottom to search

  • 25 = Search in bottom 25% of document (default, professional)
  • 40 = Search in bottom 40% (more flexible)
  • 50 = Search in bottom 50% (entire lower half)

• right_percent: Controls how far from the right to search

  • 50 = Search in right 50% of document (default, typical signature position)
  • 40 = Search in rightmost 40% (more to the right)
  • 70 = Search in right 70% (includes left-center area)

🤖 Perfect for AI & Machine Learning

SignLib is designed for generating synthetic training data:

• Consistent Quality: Generate thousands of signed documents with consistent quality
• Variation Control: Easily control position, size, rotation, and color for data diversity
• Batch Processing: Process large datasets efficiently
• Reproducible: Same parameters = same results for reproducible experiments

📝 Notes

• Signature files should be in PNG format (for transparent background support)
• Supported document formats: PDF, TIFF, PNG, JPEG
• When signature_color=None, the library auto-detects the most common dark color from the document
• Signatures are typically placed in the bottom-right area within the whitest available space
• Background is automatically removed and contrast is enhanced

🔧 Requirements

• Python 3.6+
• Pillow >= 9.0.0
• NumPy >= 1.20.0

📄 License

MIT License - feel free to use in commercial and open-source projects.

👨‍💻 Author

Cagri Gungor (@cagrigungor)

Specialized in AI training data generation and synthetic document creation.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

🐛 Issues

Found a bug or have a feature request? Please open an issue on GitHub.

📊 Example Use Case: AI Training Dataset

import random
from signlib import create_sign
from pathlib import Path

# Generate diverse training dataset
documents = list(Path('documents').glob('*.pdf'))
signatures = list(Path('signatures').glob('*.png'))

for i in range(1000):  # Generate 1000 synthetic samples
    doc = random.choice(documents)
    sig = random.choice(signatures)
    
    # Vary parameters for diversity
    create_sign(
        str(doc),
        str(sig),
        output_path=f'training_data/sample_{i:04d}.pdf',
        scale_factor=random.uniform(0.10, 0.15),
        rotation_angle=random.uniform(-10, 10),
        bottom_percent=random.randint(20, 35),
        right_percent=random.randint(40, 60)
    )

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SignLib - Professional signature placement for AI and synthetic data generation 🚀