metadata

dataset_info:
  features:
    - name: document
      dtype: image
    - name: bbox
      list:
        list: float32
    - name: to_verify_signature
      dtype: image
    - name: sample_signature
      dtype: image
    - name: label
      dtype: int32
  splits:
    - name: train
      num_bytes: 3345162323.328
      num_examples: 23206
    - name: test
      num_bytes: 831965018.26
      num_examples: 6195
  download_size: 3550853030
  dataset_size: 4177127341.5880003
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
license: apache-2.0
task_categories:
  - image-classification
  - object-detection
tags:
  - signature
  - document
pretty_name: Signature Detection and Verification
size_categories:
  - 10K<n<100K

Signature Detection and Verification Dataset

A comprehensive dataset designed for building and evaluating end-to-end signature analysis pipelines, including signature detection in document images and signature verification using genuine/forged pair classification.

Developed by: @Mels22 and @JoeCao

Pipeline Overview

This dataset supports a complete signature detection and verification pipeline. The process involves identifying the signature in a document and comparing it with a reference to determine if it is genuine or forged.

Figure 1: Detection and Verification Pipeline.

The Detection Model locates the signature in the document.
The cropped signature (to_verify_signature) is passed along with a sample signature (sample_signature) to the Verification Model.
The model then classifies the signature as either Genuine or Forged.

Dataset Summary

Split	Samples
Train	23,206
Test	6,195
Total	29,401

This dataset supports two key tasks:

Detection: Identifying the bounding boxes of signatures in scanned document images.
Verification: Comparing a signature within the document to a reference (sample) signature to determine whether it's genuine (label = 0) or forged (label = 1).

Features

Each sample in the dataset contains the following fields:

document (Image): The full document image that contains one or more handwritten signatures.
bbox (List of Bounding Boxes): The coordinates of the signature(s) detected in the document. Format: [x_min, y_min, x_max, y_max].
to_verify_signature (Image): A cropped signature from the document image that needs to be verified.
sample_signature (Image): A standard reference signature used for comparison.
label (int): Indicates if the to_verify_signature is genuine (0) or forged (1) when compared to the sample_signature.

Data Sources & Construction

This dataset is constructed by combining and modifying two publicly available datasets:

Signature Images were sourced from the Kaggle Signature Verification Dataset, which provides genuine and forged signatures from multiple individuals for verification tasks.
Document Images with Signature Bounding Boxes were taken from the Signature Detection Dataset by NanoNets, which contains scanned documents with annotated signature regions.

How This Dataset Was Created

To create a seamless, unified pipeline dataset for detection + verification, the following modifications were made:

Synthetic Placement: Signature images were programmatically inserted into real documents at their correct signing regions (e.g., bottom of the page or designated signature lines).
Blending with Background: Signatures were rendered with varying opacities, filters, and transformations to match the document background, mimicking real-world signature scans.
Labeling and BBoxes: The new locations of the inserted signatures were used to generate accurate bounding boxes for detection tasks.
Pairing for Verification: Each inserted signature (to_verify_signature) was paired with a reference (sample_signature) and assigned a label: 0 for genuine or 1 for forged.

This process enables researchers to train and evaluate models for both signature localization and signature verification in a realistic, document-centric setting.

Sample Code

from datasets import load_dataset
data = load_dataset("Mels22/SigDetectVerifyFlow")

for i, example in enumerate(data['train']):
    example['document'].show()
    example['to_verify_signature'].show()
    example['sample_signature'].show()
    print(f"Bbox: {example['bbox']}")
    print(f"Label: {example['label']}")
    break