Multi-scripted Writer Independent Off-line Signature Verification using Convolutional Neural Network

Signature is a biometrics trait widely used for personal verification in financial and most other organizations where financial and relevant types of transactions are done manually with signed authorized papers. A signature verification system aims to verify the minor structural differences between genuine and forged signatures, as most skilled forgery signatures look similar to their respective genuine signatures with specific deformations. Signature verification in a multi-cultural country like India is challenging in both writer-independent and script-independent scenarios where the Indian population uses multiple scripts to write their signatures. This paper reports a writer-independent offline signature verification system that uses Convolutional Neural Network (CNN) architecture for feature extraction and classification. The objective of the proposed work is to model a CNN-based adaptable system to verify multi-scripted offline signatures. The model has been trained and tested on two publicly available databases, viz. CEDAR and BH-Sig260, which consist of Hindi, Bengali, and English signatures. Individual signature classes of unique scripts and a combination of these scripts have been considered for testing the proposed model that determines verification accuracies of 90%, 95%, 98.33%, and 93.33%, respectively. Experimental results are compelling, and the proposed model outperforms the verification accuracies of some well-known models.