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

Key Points

• Signature verification accuracy improved by 15% using a hybrid machine learning approach, indicating enhanced security.
• A database of 1,000 signature samples was utilized to evaluate the model's performance.
• Hybrid machine learning framework combines multiple algorithms and gray wolf optimization for feature extraction.
• This method may provide better results compared to traditional verification techniques, showing promise in practical applications.