SIGNSECURE: Handwritten Signature Verification
Renuka Narkhede
, Rutuja Sangole , Shalvi Dangre , Minakshi Getkar , Achal Thakare
In many educational institutions, hostel wardens rely on manually signed leave applications, making it difficult to verify whether the handwritten signature of the Head of Department (HOD) is genuine or forged. This project addresses that challenge by developing an automated Handwritten Signature Verification System using a Convolutional Neural Network (CNN) based Siamese architecture. The system analyzes and compares signature patterns to accurately distinguish between authentic and forged signatures. To ensure security and integrity, all signature data is encrypted, and verification results are stored on a blockchain ledger, making records tamper-proof and auditable. The complete solution is deployed on AWS cloud, enabling scalable access and reliable performance. With future plans to integrate quantum-secure encryption, this system aims to provide a robust, trustworthy, and technologically advanced method of signature verification in academic and administrative environments.
"SIGNSECURE: Handwritten Signature Verification", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.11, Issue 1, page no.b510-b515, January-2026, Available :https://ijsdr.org/papers/IJSDR2601166.pdf
Volume 11
Issue 1,
January-2026
Pages : b510-b515
Paper Reg. ID: IJSDR_307105
Published Paper Id: IJSDR2601166
Downloads: 000213
Research Area: Science and Technology
Country: Nagpur, Maharashtra, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
