A Novel Technique for Handwritten Signature Recognition

Leila Boucerredj; Karima Kechroud; Bouaziz Noureddine; Abderrahmane Khechekhouche

doi:10.18540/jcecvl10iss8pp20810

Authors

Leila Boucerredj Department of Electronics and Automatics, Faculty of Science and Technology, PIMIS, Laboratory, University of 8 Mai 1945, PO Box 401, GUELMA 24000, Algeria https://orcid.org/0000-0002-8481-7725
Karima Kechroud Department of Electronics and Automatics, Faculty of Science and Technology, PIMIS, Laboratory, University of 8 Mai 1945, PO Box 401, GUELMA 24000, Algeria
Bouaziz Noureddine Department of Electronics and Automatics, Faculty of Science and Technology, PIMIS, Laboratory, University of 8 Mai 1945, PO Box 401, GUELMA 24000, Algeria https://orcid.org/0000-0002-2376-1916
Abderrahmane Khechekhouche Laboratory (LNTDL), Faculty of Technology, University of El Oued, Algeria https://orcid.org/0000-0002-7278-2625

DOI:

https://doi.org/10.18540/jcecvl10iss8pp20810

Keywords:

Biometric Recognition, Deep Learning (DL), Handwritten Signatures, CNN, MCYT-75 and GPDS-300 database.

Abstract

Handwritten signature recognition (HSR) is a critical component of biometric systems, widely used for securing financial transactions and identity verification. However, the variability of handwritten signatures, influenced by individual writing styles, inconsistencies, and environmental factors, presents significant challenges for recognition systems. Despite these obstacles, signatures remain a reliable and popular biometric trait. This paper introduces a novel deep learning approach utilizing a convolutional neural network (CNN) architecture specifically designed for HSR. The proposed method was validated using two prominent datasets, MCYT-75 and GPDS-300, with detailed descriptions of the CNN structure. Experiments, conducted on a personal computer equipped with an NVIDIA Quadro M1200 GPU, an Intel i7 processor, and 32 GB of RAM, demonstrated the model’s exceptional performance, achieving validation accuracies of 99.60% on the MCYT-75 dataset and 99.80% on the GPDS-300 dataset. These results reflect the model’s robustness and minimized error rates, outperforming existing techniques and underscoring the effectiveness of deep learning for signature recognition. This study highlights the proposed model's potential for real-world applications and paves the way for further advancements in biometric authentication technologies.

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