Open Access
An Offline Signature Verification System Using Neural Network Based on Angle Feature and Energy Density
Rahul Sharma,Manish Shrivastav +1 more
TLDR
This work proposes an intelligent network that works on the features like angle feature and energy density of the signature for the verification and also a comparative statement is made between them in order to see which method provides better results.Abstract:
Hand written signature used every day at various places for the authentication of a person, but a signature of a person may not be same at different time or it may be generated by some fraud way. So, a system is required for verification of the signature. The signature verification can be done either online or offline, here we are using offline signature verification network. In the proposed system the signatures is taking as an image and apply image processing technique to make the system effective. Here we propose an intelligent network that works on the features like angle feature and energy density of the signature for the verification and also a comparative statement is made between them in order to see which method provides better results.read more
Citations
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Offline Signature Verification and Identification Using Angle Feature and Pixel Density Feature And Both Method Together
TL;DR: An intelligent neural network that work on the feature like pixel density method, angular method and mix both methods together is proposed and compared these methods to see which one method provides the better result and accuracy.
Journal Article
Online Signature Verification Using Energy,Angle and Directional Gradient Feature WithNeural Network
Priyank Jain,Jayesh Gangrade +1 more
TL;DR: In this paper the signatures is taking as a image by the signature pad and apply image processing technique before the feature extraction to make the system effective, the angle, energy and chain code features are used in this paper to differentiate the signature.
Journal Article
Leaf Recognition Using Feature Point Extraction and Artificial Neural Network
TL;DR: The objective of this project is to identify the accurate input leaf for feature extraction; two schemes are 28 and 60 feature point extraction.
Review on offline signature verification methods based on artificial intelligence technique
Imran Hussain,Vikash Shrivastava +1 more
TL;DR: Author wants to illustrate two techniques reviewed by him on Offline signature Verification that are mixed of Energy with Angle and Energy with Chain Code.
iLeaf: Leaf based Tree Recognition System
TL;DR: This work aims to put Smartphone or tablets owners more familiar with nature around them and to sense the power of citizens to map the various spe cies and the diversity of trees with more detail information.
References
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Journal ArticleDOI
Automatic Signature Verification: The State of the Art
Donato Impedovo,Giuseppe Pirlo +1 more
TL;DR: This paper presents the state of the art in automatic signature verification and addresses the most valuable results obtained so far and highlights the most profitable directions of research to date.
Journal ArticleDOI
Automatic signature verification: the state of the art—1989–1993
Franck Leclerc,Réjean Plamondon +1 more
TL;DR: This paper summarizes the activity from year 1989 to 1993 in automatic signature verification and reports on the different projects dealing with dynamic, static and neural network approaches.
Journal ArticleDOI
Offline signature verification and identification using distance statistics
TL;DR: This paper describes a novel approach for signature verification and identification in an offline environment based on a quasi-multiresolution technique using GSC (Gradient, Structural and Concavity) features for feature extraction using a mapping from the handwriting domain to the signature domain.
Journal ArticleDOI
A new signature verification technique based on a two-stage neural network classifier
Haris Baltzakis,Nikos Papamarkos +1 more
TL;DR: A new technique for off-line signature recognition and verification based on global, grid and texture features and implemented in a special two stage Perceptron OCON (one-class-one-network) classification structure.
Proceedings ArticleDOI
Off-line signature verification using HMM for random, simple and skilled forgeries
TL;DR: The experiments have shown that the error rates of the simple and random forgery signatures are very closed, and this reflects the real applications in which the simple forgeries represent the principal fraudulent case.
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