OSVNet: Convolutional Siamese Network for Writer Independent Online Signature Verification
Chandra Sekhar, Prerana Mukherjee, Devanur S Guru, Viswanath, Pulabaigari

TL;DR
This paper introduces OSVNet, a deep convolutional Siamese network framework for online signature verification that effectively learns intra-personal variations and improves accuracy over existing methods.
Contribution
The paper proposes a novel deep convolutional Siamese network for online signature verification, extending it for one-shot learning and demonstrating superior performance on benchmark datasets.
Findings
Achieves lower error rates than recent state-of-the-art OSV techniques.
Effectively reduces intra-writer variability and enhances inter-writer discrimination.
Demonstrates robustness across multiple benchmark datasets.
Abstract
Online signature verification (OSV) is one of the most challenging tasks in writer identification and digital forensics. Owing to the large intra-individual variability, there is a critical requirement to accurately learn the intra-personal variations of the signature to achieve higher classification accuracy. To achieve this, in this paper, we propose an OSV framework based on deep convolutional Siamese network (DCSN). DCSN automatically extracts robust feature descriptions based on metric-based loss function which decreases intra-writer variability (Genuine-Genuine) and increases inter-individual variability (Genuine-Forgery) and directs the DCSN for effective discriminative representation learning for online signatures and extend it for one shot learning framework. Comprehensive experimentation conducted on three widely accepted benchmark datasets MCYT-100 (DB1), MCYT-330 (DB2) and…
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Taxonomy
TopicsHandwritten Text Recognition Techniques · Digital and Cyber Forensics · Topic Modeling
MethodsSiamese Network
