Signature Verifier System
Year: 2025
Type: article
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.
Authors: Prof. Girish Patil, Mr. Yash Kale, Samarth Kadam, Hrishikesh Gavane, Dr.Devidas S. Thosar
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6949615223
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