Unveiling the Future of Signature Verification: Deep Learning Insights
Keywords:
Offline Signature Verification, Deep Learning, Genuine or Forged, Dual Path Network, Convolutional Neural NetworkAbstract
A person's signature, which is usually affixed to documents as proof of approval or authority, is a unique handwritten representation of their identity. Signatures serve as a useful means of personal identification and are essential for the validation of transactions, contracts, and official documents. Strong signature verification techniques are necessary to counter the growing threat of forged signatures as the use of signatures in diverse areas increases. Forged signatures and fraudulently created copies significantly affect the security and authenticity of documents. This paper suggests a novel deep learning technique for offline signature verification for ease this concern. This novel method seeks to improve the model's capacity to extract specific details as well as high-level semantic information from signature images. A variety of real and fake signatures are included in the dataset, and thorough preprocessing and augmentation methods are used to guarantee successful model training. With 97.39% accuracy, 96.79% precision, 97% recall, and 97.20% F1-Score, the suggested model performs remarkably well. These findings demonstrate the efficiency of the deep learning-based technique in precisely confirming signatures and identifying suspected forged.
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