Handwritten Signature Recognition System

Registration ID: IJNRD_191819

Published ID: IJNRD2304433

DOI: Click Here to Get

Sudhanshu Sharma , Tanmay Pareek , Niket Sharma , Deepak moud

Convolution Neural Network, VGG16 Model, Signature Authentication, Signature Verification, Transfer Learning

In today’s generation, machine learning researchers are successfully studying the verification and validation of handwritten signatures. Each individual has a distinct signature that is primarily utilized for reasons related to private verification and identification of significant papers or legal transactions. Finding out whether the signature is genuine or fake is the goal of signature authentication. The aim of this paper is to gain multi-class classification and high accuracy with a sample of training signatures. Using a variety of image processing methods, images are preprocessed to separate the signature elements from the background pixels. In this paper, for the creation of the model we have used 24 genuine signatures from 75 different users and 10 forged signatures from 75 different users. For the classification of names and real–forged classification, we have used a pre-trained network transfer learning named the VGG16 model in which 1.2 million images were used to train a convolutional neural network to classify 1000 distinct categories.

Published Paper   E-Certificate

Sudhanshu Sharma, Tanmay Pareek, Niket Sharma, & Deepak moud (April-2023). Handwritten Signature Recognition System. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(4), e240-e249. https://ijnrd.org/papers/IJNRD2304433.pdf

Volume 8 Issue 4, April-2023

Pages : e240-e249

Paper Reg. ID: IJNRD_191819

Published Paper Id: IJNRD2304433

Downloads: 000121989

Research Area: Engineering

Country: Chittorgarh, rajasthan , India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2304433.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2304433

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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