What question did this study set out to answer?

The central aim is to explore deep learning techniques for effectively verifying handwritten signatures.

February 21, 2026Open Access

Deep Transfer Learning based Handwritten Signature Verification

Key Points

The central aim is to explore deep learning techniques for effectively verifying handwritten signatures.
Applied deep learning via transfer learning using CNNs
Used VGG-16, VGG-19, and Alexnet as feature extractors
Utilized CEDAR and ICDAR 2011 datasets for training
Trained models to classify real vs. forged signatures
Achieved up to 100% precision in model performance
Alexnet model demonstrated the highest accuracy among the three
Recorded lowest training cost for the Alexnet model

Abstract

In past few decades, handwritten verification system has got a lot of attention, but that's still a continuing process. Signature verification techniques are being used to determine whether such a signature is real or forged i.e., produced by an impostor. Many enhancements have certainly been suggested in the literature a most notable of which being use of Deep Learning algorithms to learn image attributes from signature images. As an explorative study, we trained models based on the principle of transfer learning using three state-of-the-art CNNs, namely VGG-16, VGG-19 and Alexnet as feature extractors and classifiers. The CEDAR Dataset, the ICDAR 2011 Dataset and the combination of both the CEDAR and the ICDAR 2011 Datasets are the three datasets considered for the proposed methodology. The models showed performance, with the highest precision reaching up to 100%. Among the three models, the Alexnet model exhibited the highest accuracy and lowest training cost.

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