scholarly journals SIGNATURE VERIFICATION USING SIAMESE NEURAL NETWORK ONE-SHOT LEARNING

2021 ◽  
Author(s):  
Merve VAROL ARISOY
Keyword(s):  
Neural Network ◽  
Signature Verification

scholarly journals Online signature verification using neural network and pearson correlation features

2013 ◽  
Author(s):  
Vahab Iranmanesh ◽  
Sharifah Mumtazah Syed Ahmad ◽  
Wan Azizun Wan Adnan ◽  
Fahad Layth Malallah ◽  
Salman Yussof
Keyword(s):  
Neural Network ◽  
Pearson Correlation ◽  
Signature Verification ◽  
Online Signature ◽  
Online Signature Verification

AUTOMATIC SIGNATURE VERIFICATION: THE STATE OF THE ART—1989–1993

1994 ◽  
Vol 08 (03) ◽  
pp. 643-660 ◽  
Author(s):  
FRANCK LECLERC ◽  
RÉJEAN PLAMONDON
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
The State ◽  
Signature Verification ◽  
Writer Identification ◽  
Network Approaches

This paper is a follow up to an article published in 1989 by R. Plamondon and G. Lorette on the state of the art in automatic signature verification and writer identification. It summarizes the activity from year 1989 to 1993 in automatic signature verification. For this purpose, we report on the different projects dealing with dynamic, static and neural network approaches. In each section, a brief description of the major investigations is given.

Offline Signature Verification and Forgery Detection Based on Computer Vision and Fuzzy Logic

2014 ◽  
Vol 3 (4) ◽  
pp. 156
Author(s):  
Gautam S. Prakash ◽  
Shanu Sharma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Detection System ◽  
Structural Parameters ◽  
Signature Verification ◽  
Width Ratio ◽  
Forgery Detection ◽  
Offline Signature Verification ◽  
Artificial Neural

<p>Automated signature verification and forgery detection has many applications in the field of Bank-cheque processing,document  authentication, ATM access etc. Handwritten signatures have proved to be important in authenticating a person's identity, who is signing the document. In this paper a Fuzzy Logic and Artificial Neural Network Based Off-line Signature Verification and Forgery Detection System is presented. As there are unique and important variations in the feature elements of each signature, so in order to match a particular signature with the database, the structural parameters of the signatures along with the local variations in the signature characteristics are used. These characteristics have been used to train the artificial neural network. The system uses the features extracted from the signatures such as centroid, height – width ratio, total area, I<sup>st</sup> and II<sup>nd</sup> order derivatives, quadrant areas etc. After the verification of the signature the angle features are used in fuzzy logic based system for forgery detection.</p>

Sign in / Sign up

Export Citation Format

Share Document