Scholarlink

<p>Handwritten signature verification is one of the frequently used biometric in administrative, financial, legal, and similar scenarios to verify the identity of a person. Unfortunately, the offline nature of this task makes it more challenging as it involves intra-signature variance, and various temporal and environmental factors. This paper presents a novel offline signature verification system (OSVS) called SignGuard. It is designed by using Gray Wolf Optimization (GWO) for preprocessing. Also, Principal Orientation Alignment (POA) is used to mitigate their rotation as the proposed method has rotation sensitive descriptors. It is followed by two writer-independent models with new texture features namely, Centre Symmetric local binary pattern (CS-LBP) and Orthogonal Central Symmetric Local Binary Pattern (OC-CSLBP). They are trained using hybrid machine learning framework such that Support Vector Machine and XGBoost classifiers are integrated for the verification of a signature image in an offline mode. The performance of SignGuard is exploited on CEDAR, SID, and BHSig260 datasets, along with a novel dataset called DeepSignVault, such that OC-CSLBP and CS-LBP yields an accuracy of 98.77% and 97.46%, respectively. It is observed that SignGuard outperforms the existing OSVSs. The proposed architecture shows enhanced security and reliability for real-world applications in business, legal, and administrative systems. The outstanding performance of SignGuard highlights the authenticity of legal documents and financial transactions.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A hybrid machine learning framework for offline signature verification using gray wolf optimization

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月3日

查看全文
  • Nemi Chandra Rathore,
  • Akshay Juneja,
  • Neeraj Kumar,
  • Vijay Kumar,
  • Arvind Dhaka

摘要

Handwritten signature verification is one of the frequently used biometric in administrative, financial, legal, and similar scenarios to verify the identity of a person. Unfortunately, the offline nature of this task makes it more challenging as it involves intra-signature variance, and various temporal and environmental factors. This paper presents a novel offline signature verification system (OSVS) called SignGuard. It is designed by using Gray Wolf Optimization (GWO) for preprocessing. Also, Principal Orientation Alignment (POA) is used to mitigate their rotation as the proposed method has rotation sensitive descriptors. It is followed by two writer-independent models with new texture features namely, Centre Symmetric local binary pattern (CS-LBP) and Orthogonal Central Symmetric Local Binary Pattern (OC-CSLBP). They are trained using hybrid machine learning framework such that Support Vector Machine and XGBoost classifiers are integrated for the verification of a signature image in an offline mode. The performance of SignGuard is exploited on CEDAR, SID, and BHSig260 datasets, along with a novel dataset called DeepSignVault, such that OC-CSLBP and CS-LBP yields an accuracy of 98.77% and 97.46%, respectively. It is observed that SignGuard outperforms the existing OSVSs. The proposed architecture shows enhanced security and reliability for real-world applications in business, legal, and administrative systems. The outstanding performance of SignGuard highlights the authenticity of legal documents and financial transactions.