Abstract

This paper introduces an attention-guided joint CNN–neuro-fuzzy learning framework for offline handwritten signature verification, in which convolutional feature learning and fuzzy membership optimization are performed simultaneously rather than through a conventional sequential pipeline. Grad-CAM–based attention cues are incorporated to guide the adaptive update of fuzzy membership functions, allowing the model to emphasize discriminative signature regions during decision making. To improve robustness across scripts and writing styles, a domain-adaptive training strategy is adopted, enabling effective generalization across English, Bengali, and Hindi signatures. Experiments conducted on the CEDAR, GPDS, and BHSig datasets demonstrate consistent improvements in cross-dataset transfer accuracy over baseline CNN, fuzzy-only, and conventional CNN–fuzzy approaches. The proposed joint learning strategy enhances model interpretability by linking attention-derived feature importance with fuzzy inference rules, while domain adaptation contributes to improved resilience against variations in script, writer style, and acquisition conditions. Overall, the framework provides a step toward more explainable and multilingual signature verification systems.

Authors

Sunil Tanaji Salunkhe, Suhas Surykantrao Satonkar
Swami Ramanand Teerth Marathwada University, India

Keywords

CNN, Neuro-Fuzzy Systems, Joint Learning, Grad- CAM, Domain Adaptation, Cross-Language Biometrics