• Meduri Jahnavi
• Konakanchi. Vishnu vamsi
• Gundavarapu. Divya bhanu
• Midde. Bharath kumar

This paper presents, Signature forgery detection using neural network based deep learning algorithms, aiming to increase the accuracy in training and prediction. The proposed system employs multilayer neural networks. By leveraging the power of deep learning, the model learns complex patterns and variations in signatures, enabling accurate differentiation between authentic and forged signatures. Experimental results demonstrate the effectiveness of the proposed approach in accurately identifying forged signatures with high precision, The proposed implementation of neural network-based deep learning algorithms offers several advantages over traditional methods, including enhanced accuracy, efficiency, and scalability. By automating the signature forgery detection process, the system enables faster decision-making, reduces manual workload, and strengthens document security in digital transactions and administrative workflows. Overall, this project aims to contribute to the advancement of signature verification technologies.

signature, forgery, multilayer perceptron, adam optimizer

"Signature Forgery Detection using Neural Network based deep learning algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.l296-l300, April-2024, Available :http://www.jetir.org/papers/JETIR2404B40.pdf

"Signature Forgery Detection using Neural Network based deep learning algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppl296-l300, April-2024, Available at : http://www.jetir.org/papers/JETIR2404B40.pdf