Offline handwritten signature identification using adaptive window positioning techniques

TL;DR

This paper introduces an adaptive window positioning technique for offline handwritten signature identification, focusing on individual writer traits and tested on a large dataset, showing high accuracy and potential for emotional stress detection.

Contribution

It proposes a novel adaptive window technique for signature feature extraction, improving identification accuracy and reliability over existing methods.

Findings

Effective signature feature extraction method

High identification accuracy on GPDS dataset

Potential for detecting signatures under emotional duress

Abstract

The paper presents to address this challenge, we have proposed the use of Adaptive Window Positioning technique which focuses on not just the meaning of the handwritten signature but also on the individuality of the writer. This innovative technique divides the handwritten signature into 13 small windows of size nxn(13x13).This size should be large enough to contain ample information about the style of the author and small enough to ensure a good identification performance.The process was tested with a GPDS data set containing 4870 signature samples from 90 different writers by comparing the robust features of the test signature with that of the user signature using an appropriate classifier. Experimental results reveal that adaptive window positioning technique proved to be the efficient and reliable method for accurate signature feature extraction for the identification of offline handwritten signatures.The contribution of this technique can be used to detect signatures signed under emotional duress.