Results 71 to 80 of about 609,261 (188)

Offline Handwritten Signature Verification Using a Stream-Based Approach

open access: yes
Handwritten Signature Verification (HSV) systems distinguish between genuine and forged signatures. Traditional HSV development involves a static batch configuration, constraining the system's ability to model signatures to the limited data available. Signatures exhibit high intra-class variability and are sensitive to various factors, including time ...
Kecia Gomes de Moura   +2 more
openaire   +3 more sources

Offline Signature Verification Using the Discrete Radon Transform and a Hidden Markov Model

open access: yesEURASIP Journal on Advances in Signal Processing, 2004
We developed a system that automatically authenticates offline handwritten signatures using the discrete Radon transform (DRT) and a hidden Markov model (HMM).
du Preez JA, Coetzer J, Herbst BM
doaj   +1 more source

A systematic study of offline recognition of Thai printed and handwritten characters

open access: yes, 2011
Thai characters pose some unique problems, which differ from English and other oriental scripts. The structure of Thai characters consists of small loops combined with curves and there is an absence of spaces between each word and sentence. In each line,
Sae-Tang, Sutat
core   +1 more source

Embedding Riemannian Collective Background Knowledge for Offline Signature Verification

open access: yesMachine Learning and Knowledge Extraction
While handwritten signatures are a staple of biometric authentication, conventional verification models typically rely on Euclidean space assumptions, restricting the capture of complex, intrinsic signature structures.
Evangelos Mitikas   +2 more
doaj   +1 more source

An examination of quantitative methods for Forensic Signature Analysis and the admissibility of signature verification system as legal evidence. [PDF]

open access: yes, 2015
The experiments described in this thesis deal with handwriting characteristics which are involved in the production of forged and genuine signatures and complexity of signatures.
Chatzisterkotis, Thomas
core  

Handwritten Signature Generation Using Denoising Diffusion Probabilistic Models with Auxiliary Classification Processes

open access: yesApplied Sciences
Automatic signature verification has been widely studied for authentication purposes in real life, but limited data availability still poses a significant challenge.
Dong-Jin Hong   +2 more
doaj   +1 more source

A New Approach for High Pressure Pixel Polar Distribution on Off-line Signature Verification

open access: yesTecnoLógicas, 2010
Features representing information of High Pressure Points froma static image of a handwritten signature are analyzed for an offline verification system. From grayscale images, a new approach for High Pressure threshold estimation is proposed. Two images,
Jesús F. Vargas   +3 more
doaj  

Research on Detecting AI-Generated Forged Handwritten Signatures via Data-Efficient Image Transformers

open access: yesIEEE Access
Handwritten signature verification, a behavioral physiological biometric, is widely accepted in legal and social practices. However, the advent of artificial intelligence (AI) and its application in handwriting synthesis, known as AI-generated content ...
Yanli Hao, Zuyang Zheng
doaj   +1 more source

Off line Handwritten Signature Recognition based on Fusion of Global and GLCM Features Using Fuzzy Logic

open access: yesمجلة علوم ذي قار, 2019
Signature is widely used and developed area of research for personal verification and authentication. In this paper, we present a new offline handwritten signature recognition system based on fusion of global and GLCM (Grey Level Co-occurrence Matrix ...
Zamen F. Jabur
doaj  

AI-Driven Forgery Detection in Offline Handwriting Signatures: Advances, Challenges, and the Role of Generative Adversarial Networks

open access: yesJournal of Computing Research and Innovation
Handwriting-based authentication continues to be a critical element in forensic analysis, particularly in the context of document fraud and signature forgery. Although deep learning (DL) techniques have shown promising results, there are still obstacles
Safura Adeela Sukiman   +3 more
doaj   +1 more source

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