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Signature Verifier System

Prof. Girish Patil, Mr. Yash Kale, Samarth Kadam, Hrishikesh Gavane, Dr.Devidas S. Thosar

Abstract


The increasing reliance on handwritten signatures for personal and legal identification makes it essential to develop secure and automated verification systems. This research presents a Signature Verifier System that uses image processing and deep learning to distinguish between genuine and forged signatures. The system is developed using Python and OpenCV for preprocessing, and ResNet50, a deep Convolutional Neural Network (CNN), for classification. The model learns the subtle features of signatures and provides accurate verification results. The proposed method offers an efficient and scalable solution for enhancing document security in sectors like banking, education, and legal services.

Keywords: ResNET50 Model, Convolutional Neural Network.


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References


Tsourounis, D., Theodorakopoulos, I., Zois, E. N., & Economou, G. (2023). Leveraging Expert Models for Training Deep Neural Networks in Scarce Data Domains: Application to Offline Handwritten Signature Verification. arXiv preprint arXiv:2308.01136. arXiv+1arXiv+1

Melzi, P., Tolosana, R., Vera-Rodriguez, R., Delgado-Santos, P., Stragapede, G., Fierrez, J., & Ortega-Garcia, J. (2023). Exploring Transformers for On-Line Handwritten Signature Verification. arXiv preprint arXiv:2307.01663. arXiv

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).

GPDS Signature Verification Dataset. Available at: http://www.gpds.ulpgc.es CEDAR Signature Dataset. Available at: http://www.cedar.buffalo.edu/NIJ/data/signatures/OpenCV Documentation. https://docs.opencv.org

Joshi, A., & Wadhwani, S. (2020). Offline Signature Verification using Deep CNN Techniques: An Indian Perspective. International Journal of Computer Applications, 177(39), 1–6.

Devidas S Thosar, Rajashree R Shinde, Prashant J Gadakh, Pratibha V Kashid, Secure kNN Query Processing in Entrusted Cloud Environments , Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, Issue I , Vol 2 (2016).

D. S. Thosar, R. D. Thosar, P. B. Dhamdhere, S. B. Ananda, U. D. Butkar and D. S. Dabhade, "Optical Flow Self-Teaching in Multi-Frame with Full-Image Warping via Unsupervised Recurrent All-Pairs Field Transform," 2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI), Wardha, India, 2024, pp. 1-4, doi: 10.1109/IDICAIEI61867.2024.10842718.

Bansal, R., & Arora, D. (2021). A Deep Learning-Based Signature Verification Model for Regional Indian Scripts. Journal of Emerging Technologies and Innovative Research (JETIR), 8(12), 54–60.

Melzi, P., Tolosana, R., Vera-Rodriguez, R., Delgado-Santos, P., Stragapede, G., Fierrez, J., & Ortega-Garcia, J. (2023). Exploring Transformers for On-Line Handwritten Signature Verification. arXiv preprint arXiv:2307.01663.

Ozyurt, F., et al. (2024). Offline Handwriting Signature Verification: A Transfer Learning and Feature Selection Approach. arXiv:2401.09467.


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