README.md

---
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](https://huggingface.co/Mels22) and [@JoeCao](https://huggingface.co/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.

<div style="text-align: center;">
    <img src="pipeline.png" alt="Detection and Verification Pipeline" style="display: block; margin: auto;">
    <div style="font-style: italic;">Figure 1: Detection and Verification Pipeline.</div>
</div>
<br>

- 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](https://www.kaggle.com/datasets/robinreni/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](https://github.com/NanoNets/SignatureDetectionDataset), 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
```python
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
```