weights-and-wires/signatures

Signatures Dataset

A collection of handwritten signature images with associated names, useful for signature generation and verification research.

Dataset Structure

This dataset contains two splits:

Columns

Both splits contain:

• —image: The signature image
• —file_name: Original filename
• —label: Numeric label
• —name: The person's name (for text conditioning)

How to use it?

from datasets import load_dataset

# Load raw (original) data
raw = load_dataset("ifkash/signatures", split="raw")

# Load processed (preprocessed for training)
processed = load_dataset("ifkash/signatures", split="processed")

# Access a sample
sample = processed[0]
print(sample['name'])   # e.g., "rafael casanova"
sample['image'].show()  # Display the signature

Processed Split Details

The processed split has been preprocessed for machine learning:

• —Resolution: 512 × 256 pixels
• —Color: Grayscale (L mode)
• —Background: White (255)
• —Signature: Black (0)
• —Aspect ratio: Preserved with white padding

Preprocessing Pipeline

1. 1.Convert to grayscale
2. 2.Invert if dark background detected
3. 3.Contrast stretching
4. 4.Resize with padding to 512×256

Quality Filters Applied

Removed from processed split:

• —Black blobs (>60% dark pixels)
• —Gray backgrounds (corner mean < 200)
• —Nearly blank images (<1% ink)
• —Corrupted/unreadable files

Use Cases

• —Text-to-Image: Generate signatures from names using models like FLUX, Stable Diffusion
• —Signature Verification: Train classifiers to verify authentic signatures
• —Style Transfer: Learn signature styles and apply to new names
• —Handwriting Analysis: Study signature characteristics

Citation

If you use this dataset, please cite:

@dataset{signatures_dataset,
  author = {ifkash},
  title = {Signatures Dataset},
  year = {2026},
  url = {https://huggingface.co/datasets/ifkash/signatures}
}