Signature Detection Dataset

Introduction

This dataset focuses on detecting human written signatures within documents. It includes a variety of document types with annotated signatures, providing valuable insights for applications in document verification and fraud detection. Essential for training computer vision algorithms, this dataset aids in identifying signatures in various document formats, supporting research and practical applications in document analysis.

Dataset Structure

The signature detection dataset is split into three subsets:

• Training set: Contains 143 images, each with corresponding annotations.
• Validation set: Includes 35 images with paired annotations.

Applications

This dataset can be applied in various computer vision tasks such as object detection, object tracking, and document analysis. Specifically, it can be used to train and evaluate models for identifying signatures in documents, which can have applications in document verification, fraud detection, and archival research. Additionally, it can serve as a valuable resource for educational purposes, enabling students and researchers to study and understand the characteristics and behaviors of signatures in different document types.

Sample Images and Annotations

The signature detection dataset comprises various images showcasing different document types and annotated signatures. Below are examples of images from the dataset, each accompanied by its corresponding annotations.

• Mosaiced Image: Here, we present a training batch consisting of mosaiced dataset images. Mosaicing, a training technique, combines multiple images into one, enriching batch diversity. This method helps enhance the model's ability to generalize across different signature sizes, aspect ratios, and contexts.

This example illustrates the variety and complexity of images in the signature Detection Dataset, emphasizing the benefits of including mosaicing during the training process.