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Journal articles on the topic 'Genuine signature'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Et. al., Kritika Vohra,. "Signature Verification Using Support Vector Machine and Convolution Neural Network." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 1S (2021): 80–89. http://dx.doi.org/10.17762/turcomat.v12i1s.1564.

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Signature is used for recognition of an individual. Signature is considered as a mark that an individual write on a paper for his/her identity or proof. It is used as a unique feature for identifying an individual. It is highly used in social and business functions which gives rise to verification of signature. There are chances of signature getting forged. Hence, the need to identify signature as genuine of forged is utmost important. In this paper, identification of signature as genuine or forged is done using two approaches. First approach is using SVM and second is using CNN. For SVM, pre-

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NELSON, WINSTON, WILLIAM TURIN, and TREVOR HASTIE. "STATISTICAL METHODS FOR ON-LINE SIGNATURE VERIFICATION." International Journal of Pattern Recognition and Artificial Intelligence 08, no. 03 (1994): 749–70. http://dx.doi.org/10.1142/s0218001494000395.

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Three methods for on-line signature verification are discussed in this paper. They are based on statistical models of features that summarize different aspects of signature shape and the dynamics of signature production. Two of the methods are based on the feature statistics of genuine signatures only. Of these two methods, the simpler one using a Euclidean distance error metric was found to have superior performance when tested on a database of 919 genuine signatures and 330 forgeries. Using a procedure for selecting the individual best 10 out of 22 features, the Euclidean distance method cor

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Starovoitov, V. V., and U. Akhundjanov. "Distribution of local curvature values as a structural feature for off-line handwritten signature verification." «System analysis and applied information science», no. 2 (October 4, 2023): 49–58. http://dx.doi.org/10.21122/2309-4923-2023-2-49-58.

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In the paper, a new feature for describing a digital image of a handwritten signature based on the frequency distribution of the values of the local curvature of the signature contours, is proposed. The calculation of this feature on the binary image of a signature is described in detail. A normalized histogram of distributions of local curvature values for 40 bins is formed. The frequency values recorded as a 40-dimensional vector are called the local curvature code of the signature.During verification, the proximity of signature pairs is determined by correlation between curvature codes and

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Mandal, Ranju, Srikanta Pal, Partha Pratim Roy, Umapada Pal, and Michael Blumenstein. "Spatial Pyramid Matching-based Multi-script Off-line Signature Identification." Journal of the American Society of Questioned Document Examiners 18, no. 1 (2015): 69–75. https://doi.org/10.69525/jasqde.216.

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Among all of the biometric authentication systems, handwritten signatures are considered as the most legally and socially accepted attributes for personal identification. The objective of this investigation is to present an empirical contribution towards the understanding of a signature identification technique involving multi-script off-line signatures. In our experiment, SIFT (Scale-Invariant Feature Transform) descriptors with Spatial Pyramid Matching (SPM)-based approaches have been used for feature extraction of signatures written in multiple scripts. Support Vector Machines (SVMs) are em

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Sangdiah, Nana Suarna, Irfan Ali, and Dendy Indriya Efendi. "Authenticity Accuracy Improvement Through the Analysis of Signature Ownership Using Convolutional Neural Network Algorithm." Journal of Artificial Intelligence and Engineering Applications (JAIEA) 4, no. 2 (2025): 1289–93. https://doi.org/10.59934/jaiea.v4i2.900.

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This research aims to improve the accuracy of signature authenticity classification using a Convolutional Neural Network (CNN) model, implemented in a web-based application using the Flask framework. In the digital era, signature authentication has become a crucial component in maintaining data security and transaction validity. However, the classification of genuine and forged signatures presents its own challenges due to the unique variations in patterns and styles of each individual. Using a public dataset from Kaggle consisting of 1,084 signature images (620 forged and 464 genuine), the CN

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Diksha Singh, Dileep Kumar, and Poonam Prakash. "Analysis of Signature Patterns: Consistency and Distinctiveness in Handwritten Signatures for Forensic Authentication." Indian Journal of Forensic Medicine & Toxicology 18, no. 1 (2024): 26–34. http://dx.doi.org/10.37506/c15rg139.

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Handwritten signatures are unique behavioural attributes that serve as a distinct form of identification forindividuals. This study focuses on the analysis of signature patterns using various parameters, including aspect ratio, angle of the first letter with respect to the baseline, and the ratio of the area of a circle to its radius within the signature. A dataset consisting of 1200 genuine and 1200 simulated (English) signature samples from 12 individuals were examined using an image analysis tool to record measurements for these parameters. The primary objectives were to investigate the con

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Starovoitov, V. V. "Verification of the person’s dynamic signature on a limited number of samples." Informatics 21, no. 2 (2024): 94–106. http://dx.doi.org/10.37661/1816-0301-2024-21-2-94-106.

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Objectives. The goal of the research is to develop a new person-dependent method for verification of a signature of one person made on a tablet with a stylus in the presence of a limited number of signature samples of this person.Methods. The paper shows how to construct an individual pattern of the dynamic signatures of any person, which is described by points in a multidimensional feature space and is intended for subsequent verification of the authenticity of the signatures of a given person. It is constructed using 5<N<20 samples of genuine human signatures. The pattern forms a conve

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Nicolaides, Kathleen Annunziata. "Using Acceleration/Deceleration Plots in the Forensic Analysis of Electronically Captured Signatures." Journal of the American Society of Questioned Document Examiners 15, no. 2 (2012): 29–43. https://doi.org/10.69525/jasqde.191.

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A research study was conducted to determine if analysis and comparison of acceleration/deceleration plots of signature data captured by electronic signature tablets would provide meaningful evidence in an examination of electronically captured signatures. This research focused on data collected by signature tablets produced by Topaz Systems, Inc., one of the largest suppliers of digital capture devices. William Flynn, in his article “Conducting a Forensic Examination of Electronically Captured Signatures” (published in the June 2012 edition of the ASQDE Journal), noted the visual differences i

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Prajapati, Prakash Ratna, Samiksha Poudel, Madan Baduwal, Subritt Burlakoti, and Sanjeeb Prasad Panday. "Signature Verification using Convolutional Neural Network and Autoencoder." Journal of the Institute of Engineering 16, no. 1 (2021): 33–40. http://dx.doi.org/10.3126/jie.v16i1.36533.

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Signature has been one of the widely used verification biometrics out there. Handwritten signatures are used in cheques, forms, letters, applications, minutes, etc. The Signature of every individual is unique in nature, that is why it is essential that a person’s handwritten signature be uniquely identified. Signature Verification is a widely used method for authenticating any individual during absence. Human verification is prone to inaccuracy and sometimes indecisiveness. This paper presents an investigation of using Convolutional Neural Network (CNN) for Writer-Dependent models in signature

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Starovoitov, V. V. "Verification of normalized online signatures without calculating dynamic features." Informatics 21, no. 4 (2024): 72–84. https://doi.org/10.37661/1816-0301-2024-21-4-72-84.

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Objectives. Study of the method of verification of the authenticity of a human signature made on a tablet with a stylus and given three parameters: coordinates X, Y and pressure on the tablet P.Methods. N genuine dynamic human signatures are given. Data describing different signatures made by one person always have a different number of points. The main variants of normalization of the original signature data are investigated. A model of an individual image of human signatures is built without calculating dynamic features. The method of dynamic time transformation (DTW) is used to compare simi

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FOROOZANDEH, ATEFEH, YOUNES AKBARI, MOHAMMAD J. JALILI, and JAVAD SADRI. "A NOVEL AND PRACTICAL SYSTEM FOR VERIFYING SIGNATURES ON PERSIAN HANDWRITTEN BANK CHECKS." International Journal of Pattern Recognition and Artificial Intelligence 26, no. 06 (2012): 1256014. http://dx.doi.org/10.1142/s0218001412560149.

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A novel system for verifying signatures on Persian handwritten bank checks is presented, in this paper. The presented system includes two main phases called: training and verification phases. At first, the system is trained using some genuine signatures provided by each customer in training phase. Then verifying the signatures on incoming checks is carried out in the verification phase. Feature extraction step is conducted based on a new approach that uses Multitresolution box-counting (MRBC) method for estimating the fractal dimension of signatures. Here, signature verification is modeled as

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Dewhurst, Tahnee N., Kaye N. Ballantyne, and Bryan Found. "Exploring the significance of pen lifts as predictors of signature simulation behaviour." Journal of the American Society of Questioned Document Examiners 18, no. 2 (2015): 3–16. https://doi.org/10.69525/jasqde.218.

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Historically, pen lifts have been considered valuable predictors of simulation behaviour. Despite this belief, there exists only limited empirical data available to characterise the extent to which unexpected pen lifts contribute to evidence in support of simulation behaviour. This study was devised to examine the frequency with which pen-lifts are observed in a population of 2280 simulated signatures as compared to a genuine signature population of 285 signatures (by 19 authors). It was found that 12% of simulated signatures featured less pen-lifts than their comparison genuine signatures, wh

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Putz-Leszczyńska, Joanna, and Michał Kudelski. "Hidden Signature for DTW Signature Verification in Authorizing Payment Transactions." Journal of Telecommunications and Information Technology, no. 4 (June 27, 2023): 59–67. http://dx.doi.org/10.26636/jtit.2010.4.1097.

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Traditional use of dynamic time warping for signature verification consists of forming some dissimilarity measure between the signature in question and a set of “template signatures”. In this paper, we propose to replace this set with the hidden signature and use it to calculate the normalized errors of signature under verification. The approach was tested on the MCYT database, using both genuine signatures and skilled forgeries. Moreover, we present the real-world application of the proposed algorithm, namely the complete biometric system for authorizing payment transactions. The authorizatio

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Lopes, José A. P., Bernardo Baptista, Nuno Lavado, and Mateus Mendes. "Offline Handwritten Signature Verification Using Deep Neural Networks." Energies 15, no. 20 (2022): 7611. http://dx.doi.org/10.3390/en15207611.

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Prior to the implementation of digitisation processes, the handwritten signature in an attendance sheet was the preferred way to prove the presence of each student in a classroom. The method is still preferred, for example, for short courses or places where other methods are not implemented. However, human verification of handwritten signatures is a tedious process. The present work describes two methods for classifying signatures in an attendance sheet as valid or not. One method based on Optical Mark Recognition is general but determines only the presence or absence of a signature. The other

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Ganjdanesh, Yousef, Keivan Maghooli, A. Motie Nasrabadi, and Mohammad-Shahram Moein. "A NEW METHOD FOR SIGNATURE VERIFICATION BASED ON PHYSIOLOGICAL CHARACTERISTICS OF HAND MUSCLES AND TENDONS." Biomedical Engineering: Applications, Basis and Communications 29, no. 01 (2017): 1750006. http://dx.doi.org/10.4015/s1016237217500065.

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Signature authentication with static and dynamic features of signature has been studied for decades, in this paper a novel and new method based on estimating elasticity and viscoelasticity characteristics of the muscles and tendons of index finger of the right hand was presented and the angles between the finger knuckles were collected by data collection glove and the location of digital pen tip on sensitive pad is stored in computer too. With NMC model and writing required mathematical equations and inverse modeling, physiological characteristics of muscles and tendons of right hand were esti

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Tang, Yonglong. "A Genuine Random Sequential Multi-signature Scheme." Bulletin of Electrical Engineering and Informatics 3, no. 1 (2014): 55–68. http://dx.doi.org/10.11591/eei.v3i1.186.

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The usual sequential multi-signature scheme allows the multi-signers to sign the document with their own information and sequence, and the signature is not real random and secure. The paper analyzes the reasons for the insecurity of the previous multi-signature scheme, and puts forward a Genuine Random Sequential Multi-signature Scheme based on The Waters signature scheme, and the experiment proves that this scheme is a good scheme suitable for the practical application with high computing efficiency.

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Putz-Leszczyńska, Joanna. "Signature verification: A comprehensive study of the hidden signature method." International Journal of Applied Mathematics and Computer Science 25, no. 3 (2015): 659–74. http://dx.doi.org/10.1515/amcs-2015-0048.

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Abstract Many handwritten signature verification algorithms have been developed in order to distinguish between genuine signatures and forgeries. An important group of these methods is based on dynamic time warping (DTW). Traditional use of DTW for signature verification consists in forming a misalignment score between the verified signature and a set of template signatures. The right selection of template signatures has a big impact on that verification. In this article, we describe our proposition for replacing the template signatures with the hidden signature-an artificial signature which i

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Huang, Dong, and Jian Gao. "On-line Signature Verification Based on GA-SVM." International Journal of Online Engineering (iJOE) 11, no. 6 (2015): 49. http://dx.doi.org/10.3991/ijoe.v11i6.5122.

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With the development of pen-based mobile device, on-line signature verification is gradually becoming a kind of important biometrics verification. This thesis proposes a method of verification of on-line handwritten signatures using both Support Vector Data Description (SVM) and Genetic Algorithm (GA). A 27-parameter feature set including shape and dynamic features is extracted from the on-line signatures data. The genuine signatures of each subject are treated as target data to train the SVM classifier. As a kernel based one-class classifier, SVM can accurately describe the feature distributi

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Prasanna,, K. Lakshmi. "Signature Fraud Detection Using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31770.

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The use of signatures for personal identification and verification is quite common. Signatures are validated for many documents such as Bank cheques and legal transactions. The necessity for effective automated solutions for signature verification has grown as signatures are now a prerequisite for both authorization and authentication in legal activities. Two images—the original signature and the test signature—are used as input in this process. To determine whether the signature is fake or not, the characteristics that were extracted are compared, and the difference in error values between th

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Estabrooks, Colin B. "Measuring Relative Pen Pressure to Authenticate Signatures." Journal of the American Society of Questioned Document Examiners 3, no. 2 (2000): 56–64. https://doi.org/10.69525/jasqde.45.

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This study exploits the capacity of the confocal laser scanning microscope (CLSM) to accurately measure the z-axis of pen pressure indentations in paper. Depth values measured at various sites of signatures are compared to the maximum depth of each signature. By measuring these “relative” depth values of multiple genuine signatures, a writer’s master pattern of pen pressure emphasis can be uniquely portrayed in a quantified manner. “Relative” depth values of simulated and traced signatures are similarly measured and are generally found to be clearly distinguishable from genuine signatures.

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Boyadzhieva, Desislava, and Georgi Gluhchev. "A Combined Method for On-Line Signature Verification." Cybernetics and Information Technologies 14, no. 2 (2014): 92–97. http://dx.doi.org/10.2478/cait-2014-0022.

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Abstract A combined method for on-line signature verification is presented in this paper. Moreover, all the necessary steps in developing a signature recognition system are described: signature data pre-processing, feature extraction and selection, verification and system evaluation. NNs are used for verification. The influence of the signature forgery type (random and skilled) over the verification results is investigated as well. The experiments are carried out on SUsig database which consists of genuine and forgery signatures of 89 users. The average accuracy is 98.46%.

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Muhammad Ulumul Ikhsanil Huda and Kustiyono. "Identification of Digital Signature Patterns Based on The CNN Method at Almas’udiyyah Islamic Boarding School." INOVTEK Polbeng - Seri Informatika 9, no. 2 (2024): 953–62. http://dx.doi.org/10.35314/74bq6m83.

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This study aims to identify digital signature patterns using the Convolutional Neural Network (CNN) method at Al Mas’udiyyah Islamic Boarding School. Digital signatures are an essential form of authentication in electronic transactions. Using MATLAB, we developed a CNN model to classify signatures and evaluate its accuracy. The dataset comprises images of students' signatures. The research stages included collecting 60 signature images for training data and 30 signature images for testing data, which were then acquired using a scanner. The results show that the Convolutional Neural Network met

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Kumar, Rakesh, Mala Saraswat, Danish Ather, Muhammad Nasir Mumtaz Bhutta, Shakila Basheer, and R. N. Thakur. "Deformation Adjustment with Single Real Signature Image for Biometric Verification Using CNN." Computational Intelligence and Neuroscience 2022 (June 25, 2022): 1–12. http://dx.doi.org/10.1155/2022/4406101.

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Signature verification is the widely used biometric verification method for maintaining individual privacy. It is generally used in legal documents and in financial transactions. A vast range of research has been done so far to tackle different system issues, but there are various hot issues that remain unaddressed. The scale and orientation of the signatures are some issues to address, and the deformation of the signature within the genuine examples is the most critical for the verification system. The extent of this deformation is the basis for verifying a given sample as a genuine or forger

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Arivanantham, T. "Survey Report on Forgery Signature Detection System." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 226–32. https://doi.org/10.22214/ijraset.2025.72007.

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Abstract: In this review, we present an offline signature forgery detection system utilizing Convolutional Neural Networks (CNN) and Principal Component Analysis (PCA). Handwritten signatures are often forged for fraudulent purposes, necessitating robust detection methods. Our system aims to classify signatures as genuine or forged by extracting key features using CNN, which captures the intricate details of the signature, such as strokes and angles. PCA is applied to reduce the dimensionality of the feature set, ensuring efficient computation without losing critical information. This hybrid a

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Akhundjanov, Umidjon, Bakhrom Soliyev, Ahror Kayumov, Abrorjon Kholmatov, Khurshid Musayev, and Zarina Ermatova. "Distribution of local curvature values as a sign for static signature verification." E3S Web of Conferences 508 (2024): 03003. http://dx.doi.org/10.1051/e3sconf/202450803003.

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This paper proposes a new feature for describing a digital image of a handwritten signature based on the frequency distribution of local curvature values of the contours of this signature. The computation of this feature on a binary signature image is described in detail. A normalized histogram of the distributions of local curvature values for 40 intervals is generated. The frequency values, written as a 40-dimensional vector, are named the local curvature code of the signature. Experimental studies are performed on digitized images of genuine and fake signatures from two databases. The accur

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Alsuhimat, Fadi Mohammad, and Fatma Susilawati Mohamad. "Offline Signature Recognition via Convolutional Neural Network and Multiple Classifiers." International Journal of Network Security & Its Applications 14, no. 1 (2022): 43–52. http://dx.doi.org/10.5121/ijnsa.2022.14103.

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One of the most important processes used by companies to safeguard the security of information and prevent it from unauthorized access or penetration is the signature process. As businesses and individuals move into the digital age, a computerized system that can discern between genuine and faked signatures is crucial for protecting people's authorization and determining what permissions they have. In this paper, we used Pre-Trained CNN for extracts features from genuine and forged signatures, and three widely used classification algorithms, SVM (Support Vector Machine), NB (Naive Bayes) and K

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Fadi, Mohammad Alsuhimat, and Susilawati Mohamad Fatma. "OFFLINE SIGNATURE RECOGNITION VIA CONVOLUTIONAL NEURAL NETWORK AND MULTIPLE CLASSIFIERS." International Journal of Network Security & Its Applications (IJNSA) 14, no. 1 (2022): 43–52. https://doi.org/10.5281/zenodo.6131635.

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One of the most important processes used by companies to safeguard the security of information and prevent it from unauthorized access or penetration is the signature process. As businesses and individuals move into the digital age, a computerized system that can discern between genuine and faked signatures is crucial for protecting people's authorization and determining what permissions they have. In this paper, we used Pre-Trained CNN for extracts features from genuine and forged signatures, and three widely used classification algorithms, SVM (Support Vector Machine), NB (Naive Bayes) a

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Liu, Ruonan, and Yizhong Xin. "Online Handwritten Signature Verification Method Based on Uni-Feature Correlation Coefficient between Signatures." Sensors 23, no. 23 (2023): 9341. http://dx.doi.org/10.3390/s23239341.

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Online handwritten signature verification is a crucial direction of research in the field of biometric recognition. Recently, many studies concerning online signature verification have attempted to improve performance using multi-feature fusion. However, few studies have provided the rationale for selecting a certain uni-feature to be fused, and few studies have investigated the contributions of a certain uni-feature in the multi-feature fusion process. This lack of research makes it challenging for future researchers in related fields to gain inspiration. Therefore, we use the uni-feature as

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Manoj Chavan. "Handwritten online signature verification and Forgery Detection for Online Handwritten Signature using Hybrid Wavelet Transform-1 with HMM classifier." Journal of Electrical Systems 20, no. 4s (2024): 2471–78. http://dx.doi.org/10.52783/jes.2800.

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Online signature verification employs a distinctive biometric trait by utilizing both static and dynamic features extracted from 2D signature images. A hybrid wavelet transform, denoted as HWT-1 with a size of 256, is formed through the Kronecker product of two orthogonal transforms, such as DCT, DHT, Haar, Hadamard, and Kekre, each with sizes 4 and 64. This HWT facilitates signal analysis at both global and local levels, akin to traditional wavelet transforms. Specifically, HWT-1 processes the 256 samples of online handwritten signatures, yielding 128 samples that constitute the feature vecto

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Prof., Girish Patil, Yash Kale Mr., Kadam Samarth, Gavane Hrishikesh, and S. Thosar Dr.Devidas. "Signature Verifier System." Research and Applications of Web Development and Design 8, no. 2 (2025): 10–13. https://doi.org/10.5281/zenodo.15273720.

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<em>The increasing reliance on handwritten signatures for personal and legal identification makes it essential to develop secure and automated verification systems. This research presents a Signature Verifier System that uses image processing and deep learning to distinguish between genuine and forged signatures. The system is developed using Python and OpenCV for preprocessing, and ResNet50, a deep Convolutional Neural Network (CNN), for classification. The model learns the subtle features of signatures and provides accurate verification results. The proposed method offers an efficient and sc

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Prajakta, Harne, Mishra M.K, and Sodhi G.S. "Variation in Length of Signatures in Case of Simulated Forgery." Journal of Forensic Chemistry and Toxicology 4, no. 2 (2018): 83–88. http://dx.doi.org/10.21088/jfct.2454.9363.4218.2.

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Simulated or imitation forgery is one of the pervasive forgeries among the group of forgers, where genuine signature of signatory authority is available to forger and he attempts to execute by following the pictorial effect of the design of the signature by simply drawing the same. However, several factors are revealed during this act of forgery. Not every reproduction has a perfect evidence of poor line quality, retouching, and other “classic” features that may establish it as a fraud. Others, specifically those carried out when copying simple short signatures may have a line quality not very

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Fatihia, Wifda Muna, Arna Fariza, and Tita Karlita. "CNN with Batch Normalization Adjustment for Offline Hand-written Signature Genuine Verification." JOIV : International Journal on Informatics Visualization 7, no. 1 (2023): 200. http://dx.doi.org/10.30630/joiv.7.1.1443.

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Signature genuine verifications of offline hand-written signatures are critical for preventing forgery and fraud. With the growth of protecting personal identity and preventing fraud, the demand for an automatic system for signature verification is high. The signature verification system is then studied by many researchers using various methods, especially deep learning-based methods. Hence, deep learning has a problem. Deep learning requires much training time for the data to obtain the best model accuracy result. Therefore, this paper proposed a CNN Batch Normalization, the CNN architectural

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FANG, B., Y. Y. WANG, C. H. LEUNG, et al. "OFFLINE SIGNATURE VERIFICATION BY THE ANALYSIS OF CURSIVE STROKES." International Journal of Pattern Recognition and Artificial Intelligence 15, no. 04 (2001): 659–73. http://dx.doi.org/10.1142/s0218001401001052.

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In this paper, a method is proposed for offline signature verification. It is based on a smoothness criterion. It is observed that the cursive segments of forgery signatures are generally less smooth and less natural than the genuine ones, especially for those signatures that consist of cursive graphic patterns. Two approaches are proposed to extract a smoothness feature: a crossing method and a fractal dimension method. When the proposed smoothness feature is combined with other global shape features for signature verification, satisfactory results are obtained.

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Parziale, Antonio, Salvatore G. Fuschetto, and Angelo Marcelli. "Modeling Stability in On-line Signatures." Journal of Forensic Document Examination 24 (December 31, 2014): 37–46. http://dx.doi.org/10.31974/jfde24-37-46.

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A novel definition of stability regions and a new method for detecting them from on-line signatures is introduced in this paper. Building upon handwriting generation and motor control studies, the stability regions is defined as the longest similar sequences of strokes between a pair of genuine signatures. The stability regions are then used to select the most stable signatures, as well as to estimate the extent to which these stability regions are encountered in both genuine and simulated (forged) signatures, thus modeling the signing habit of a subject. Experimental results on the SUSig data

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K, Manikantha, Aishwarya R Bhat, Pavani Nerella, Pooja Baburaj, and Sharvari K S. "A Comparative Study of Transfer Learning Models for Offline Signature Verification and Forgery Detection." Journal of University of Shanghai for Science and Technology 23, no. 07 (2021): 1129–39. http://dx.doi.org/10.51201/jusst/21/07272.

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Recognising one’s identity to enter a system is called authentication. This process can take various forms where users input the system with a set of identifying credentials to access the system. Signatures belong to behavioural biometric, where the distinct features of every individual are considered in order to corroborate the person’s identity. The act of falsely imitating one’s signature biometric to impersonate and leverage access to their asset is called signature forgery. Our paper presents a comparative study of various deep learning models using Siamese architecture, over a wide catal

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Patil, Prof Girish. "Signature Verification System to Automatically Decide Whether a Scanned Signature Image is Genuine or Forged." International Journal for Research in Applied Science and Engineering Technology 13, no. 7 (2025): 1712–21. https://doi.org/10.22214/ijraset.2025.73260.

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This paper introduces a strong system for checking handwritten signatures without needing to see the person sign in real time. This system helps stop fraud in important areas like banking and legal documents. The process starts with preparing the signature image by turning it into grayscale, removing noise, making it black and white, and adjusting its size. Then, it uses two methods, Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP), to extract important features from the signature. A Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel is used to classify

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Bali, Manik. "Signature Verification System Using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30155.

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The proposed system employs a convolutional neural network (CNN) architecture for signature feature extraction and classification. Furthermore, the system integrates preprocessing modules for signature image normalization, noise reduction, and feature extraction to enhance the robustness and accuracy of the verification process. Extensive experimentation and evaluation are conducted on benchmark datasets, including the widely used Tobacco 800 dataset and Kaggle dataset, to assess the performance of the proposed system in terms of accuracy, precision, recall, and score metrics. The results demo

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Parziale, Antonio, Angelo Anatrella, and Angelo Marcelli. "Stability, Speed and Accuracy for Online Signature Verification." Journal of the American Society of Questioned Document Examiners 18, no. 2 (2015): 39–49. https://doi.org/10.69525/jasqde.221.

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We suggest a model of signature verification based upon handwriting generation studies and derive from it the characterization of the signing habits of a subject. Such characterization is given in terms of the signature’s stability regions, which are obtained by exploiting shape and temporal information conveyed by the genuine signatures captured by a writing pad. The effectiveness of the proposed method for characterizing the signing habits of a subject has been evaluated in a signature verification experiment on the Sabaci University Signatures (SUSIG) database. The experimental results, obt

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Yagvalya, Gaurav. "A Novel Method of Fake Signature Detection Using Deep Learning Techniques." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33985.

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In today's digital world, making sure signatures are real is super important. Sometimes, though, people fake signatures, causing problems for money and legal stuff. This research looks into fixing this issue by finding a new way to catch fake signatures. We know that nowadays, a lot of things happen online, like signing documents and doing money stuff, and we need to make sure signatures are real. This paper introduces a fresh perspective by leveraging Convolutional Neural Networks (CNN), a deep learning technique. The objective is to enable the CNN model to autonomously learn and distinguish

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Merlino, Mara L., Veronica B. Dahir, Charles P. Edwards, et al. "Cognitive Human Factors and Forensic Document Examiner Methods and Procedures." Journal of the American Society of Questioned Document Examiners 23, no. 1 (2020): 9–30. https://doi.org/10.69525/jasqde.262.

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Here we report initial findings from an interdisciplinary study to empirically explore the reliability, measurement validity, and accuracy of established FDE methods and procedures, and to investigate the influence of possible sources of cognitive bias in the methods and procedures of forensic handwriting examination. This article reports findings of our analysis of the relationship between the position of the known signatures and the utilization of writing features in questioned/known signature comparison tasks. Forty-nine professional forensic document examiners from government labs and priv

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Manoj Chavan. "Comparative Analysis of Handwritten Online Signature Verification and Forgery Detection Using Hybrid Wavelet Transform-1 and 2 with HMM Classifier." Journal of Electrical Systems 20, no. 4s (2024): 2453–62. http://dx.doi.org/10.52783/jes.2798.

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Online signature verification is a unique biometric feature. Provides static and dynamic features for 2D signature images. Hybrid wavelet transform -1 and 2 (HWT-1 and HWT-2) of size 256 is created using the Kronecker product of two orthogonal transforms such as DCT, DHT, Haar, Hadamard and Kekre with size 4 and 64. HWT has the ability to analyze signals such as wavelet transform at global and local levels. HWT-1 and HWT-2 are used for the 256 samples of the online Handwritten signature and the first 128 samples of the output are used as feature vectors for handwritten online signature verific

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ANJALI. A, Ms. "A Comparative Study of Traced and Simulated Forgery on Signature." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem46372.

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Abstract - Signature forgery remains an important issues in the field of forensic document analysis especially in financial, legal and administrative scenarios. This study compares two forms of signature forgery that is traced and simulated by manually. The objective of this study is to analyze and differentiate the class and individual characteristics of traced and simulated forgery on signature. For the analysis, 40 genuine signature samples are collected from random persons of age groups of 18 to 25. Then performed the traced and simulated forgery. This study helps to identify which type of

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KAMEL, NIDAL S., and SHOHEL SAYEED. "SVD-BASED SIGNATURE VERIFICATION TECHNIQUE USING DATA GLOVE." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 03 (2008): 431–43. http://dx.doi.org/10.1142/s0218001408006387.

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Data glove is a new dimension in the field of virtual reality environments, initially designed to satisfy the stringent requirements of modern motion capture and animation professionals. In this paper, we try to shift the implementation of data glove from motion animation towards signature verification problem, making use of the offered multiple degrees of freedom for each finger and for the hand as well. The proposed technique is based on the Singular Value Decomposition (SVD) in finding r singular vectors sensing the maximal energy of glove data matrix A, called principal subspace, and thus

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YOSHIMURA, ISAO, and MITSU YOSHIMURA. "OFF-LINE VERIFICATION OF JAPANESE SIGNATURES AFTER ELIMINATION OF BACKGROUND PATTERNS." International Journal of Pattern Recognition and Artificial Intelligence 08, no. 03 (1994): 693–708. http://dx.doi.org/10.1142/s0218001494000371.

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A system of off-line signature verification composed of a preprocessing stage and a verification stage is proposed in this paper. It is assumed that each signature is written on a surface with a background pattern and a sample of the background pattern is available. The preprocessing stage to eliminate the background pattern from a signature image consists of 5 steps: the position adjustment between a signature image and a background image, the filtering of the background pattern by local maximization, the clipping of random noises from the background pattern, the background elimination by tra

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A, Manimegalai. "Enhancing Signature Forgery Detection System Using CNN-SVM." International Journal of Innovative Research in Information Security 10, no. 04 (2024): 288–93. http://dx.doi.org/10.26562/ijiris.2024.v1004.33.

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Handwritten signatures are widely used as a means of personal identification and authentication. Many documents like bank cheques and legal transactions require signature verification. But considering a large number of documents, it is a very difficult and time-consuming task. Therefore, ensuring the necessity for a robust automatic signature verification tool that aims to reduce fraud in all related financial transaction sectors. The current visual verification depends mainly on the experience, mood, and working environment of the verifier which ultimately wastes both time and money. Moreover

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Nuhu, A.S., N. Adam, A.M. Gadam, and D.D. Dajab. "Multi-Layer Perceptron Neural Network for an Offline Signature Verification System." Nigerian Research Journal of Engineering and Environmental Sciences 6, no. 1 (2021): 293–98. https://doi.org/10.5281/zenodo.5048363.

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<em>Signature verification using neural networks is characterized by the use of pre-processing techniques such as normalization, morphological operations and median filtering. In this work, an effective method for offline signature verification system based on multi-layer perceptron (MLP) was proposed. A signature can be divided into five logically connected, basic aspects or layers which are learnt by a single set of weights. The system was built based on a four-hidden layer neural network. An accuracy of 82.5% was attained in recognizing genuine and forged signatures which outperformed the s

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PAL, SRIKANTA, ALIREZA ALAEI, UMAPADA PAL, and MICHAEL BLUMENSTEIN. "SVM AND NN BASED OFFLINE SIGNATURE VERIFICATION." International Journal of Computational Intelligence and Applications 12, no. 04 (2013): 1340004. http://dx.doi.org/10.1142/s146902681340004x.

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Among all of the biometric authentication systems, handwritten signatures are considered as the most legally and socially accepted attributes for personal verification. The objective of this paper is to present an empirical contribution toward the understanding of a threshold-based signature verification technique involving off-line Bangla (Bengali) signatures. Experiments on signature verification concerning non-English signatures are an important consideration in the signature verification area. Only very few research works employing signatures of Indian script have been considered in the fi

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Wellem, Theophilus, Yessica Nataliani, and Ade Iriani. "Academic Document Authentication using Elliptic Curve Digital Signature Algorithm and QR Code." JOIV : International Journal on Informatics Visualization 6, no. 3 (2022): 667. http://dx.doi.org/10.30630/joiv.6.2.872.

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Paper-based documents or printed documents such as recommendation letters, academic transcripts, and diplomas are prone to forgery. Several methods have been used to protect them, such as watermarking, security holograms, or using paper with specific security features. This paper presents a document authentication system that utilizes QR code and ECDSA as the digital signature algorithm to protect this kind of document from counterfeiting. A digital signature is a well-known technique in modern cryptography used for providing data integrity and authentication. The idea proposed herein is to pu

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Khoh, Wee How, Ying Han Pang, and Hui Yen Yap. "In-air Hand Gesture Signature Recognition: An iHGS Database Acquisition Protocol." F1000Research 11 (March 7, 2022): 283. http://dx.doi.org/10.12688/f1000research.74134.1.

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Background With the advances in current technology, hand gesture recognition has gained considerable attention. It has been extended to recognize more distinctive movements, such as a signature, in human-computer interaction (HCI) which enables the computer to identify a person in a non-contact acquisition environment. This application is known as in-air hand gesture signature recognition. To our knowledge, there are no publicly accessible databases and no detailed descriptions of the acquisitional protocol in this domain. Methods This paper aims to demonstrate the procedure for collecting the

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Khoh, Wee How, Ying Han Pang, and Hui Yen Yap. "In-air Hand Gesture Signature Recognition: An iHGS Database Acquisition Protocol." F1000Research 11 (May 2, 2023): 283. http://dx.doi.org/10.12688/f1000research.74134.2.

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Background: With the advances in current technology, hand gesture recognition has gained considerable attention. It has been extended to recognize more distinctive movements, such as a signature, in human-computer interaction (HCI) which enables the computer to identify a person in a non-contact acquisition environment. This application is known as in-air hand gesture signature recognition. To our knowledge, there are no publicly accessible databases and no detailed descriptions of the acquisitional protocol in this domain. Methods: This paper aims to demonstrate the procedure for collecting t

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