A Novel Technique for Handwritten Signature Recognition

Handwritten signature recognition (HSR) is a critical component of biometric systems, widely used for securing financial transactions and identity verification. However, the variability of handwritten signatures, influenced by individual writing styles, inconsistencies, and environmental factors, presents significant challenges for recognition systems. Despite these obstacles, signatures remain a reliable and popular biometric trait. This paper introduces a novel deep learning approach utilizing a convolutional neural network (CNN) architecture specifically designed for HSR. The proposed method was validated using two prominent datasets, MCYT-75 and GPDS-300, with detailed descriptions of the CNN structure. Experiments, conducted on a personal computer equipped with an NVIDIA Quadro M1200 GPU, an Intel i7 processor, and 32 GB of RAM, demonstrated the model’s exceptional performance, achieving validation accuracies of 99.60% on the MCYT-75 dataset and 99.80% on the GPDS-300 dataset. These results reflect the model’s robustness and minimized error rates, outperforming existing techniques and underscoring the effectiveness of deep learning for signature recognition. This study highlights the proposed model's potential for real-world applications and paves the way for further advancements in biometric authentication technologies.

• handwritten signature recognition
• convolutional neural networks
• biometric authentication
• identity verification
• financial transaction security

• Handwritten Text Recognition Techniques
• Biometric Identification and Security
• User Authentication and Security Systems
• handwritten signature recognition
• convolutional neural networks
• biometric authentication
• identity verification
• financial transaction security
• Computer Vision and Pattern Recognition
• Signal Processing
• Information Systems
• Computer Science