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Journal articles on the topic 'Handwritten Signature Verification'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Zhao, Run, Dong Wang, Qian Zhang, Xueyi Jin, and Ke Liu. "Smartphone-based Handwritten Signature Verification using Acoustic Signals." Proceedings of the ACM on Human-Computer Interaction 5, ISS (2021): 1–26. http://dx.doi.org/10.1145/3488544.

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Handwritten signature verification techniques, which can facilitate user authentication and enable secure information exchange, are still important in property safety. However, on-line automatic handwritten signature verification usually requires dynamic handwritten patterns captured by a special device, such as a sensor-instrumented pen, a tablet or a smartwatch on the dominant hand. This paper presents SonarSign, an on-line handwritten signature verification system based on inaudible acoustic signals. The key insight is to use acoustic signals to capture the dynamic handwritten signature pat

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T. Vijayakumar. "Verification System for Handwritten Signatures with Modular Neural Networks." September 2022 4, no. 3 (2022): 211–18. http://dx.doi.org/10.36548/jaicn.2022.3.007.

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Handwritten signature is considered as one of the primary biometric processes for human verification in various applications including banking and legal documentations. In general, the handwritten signatures are verified with respect to the pressure, direction and speed followed on a plain document. However, the traditional methods of verification are less accurate and time consuming. The proposed work aims to develop a deep learning -based approach for handwritten signature verification process through a Modular Neural Network algorithm. The work utilized the handwritten signatures dataset do

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Xiao, Wanghui, and Yuting Ding. "A Two-Stage Siamese Network Model for Offline Handwritten Signature Verification." Symmetry 14, no. 6 (2022): 1216. http://dx.doi.org/10.3390/sym14061216.

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Offline handwritten signature verification is one of the most prevalent and prominent biometric methods in many application fields. Siamese neural network, which can extract and compare the writers’ style features, proves to be efficient in verifying the offline signature. However, the traditional Siamese neural network fails to represent the writers’ writing style fully and suffers from low performance when the distribution of positive and negative handwritten signature samples is unbalanced. To address this issue, this study proposes a two-stage Siamese neural network model for accurate offl

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Akhundjanov, Umidjon. "VERIFICATION OF STATIC SIGNATURE USING CONVOLUTIONAL NEURAL NETWORK." Al-Farg'oniy avlodlari 1, no. 4 (2023): 70–74. https://doi.org/10.5281/zenodo.10333369.

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This article is devoted to the development of a method that provides verification of handwritten signatures based on real samples obtained by scanning with a resolution of 800 dpi. Handwritten signature remains one of the most common identification methods and consideration of the problems of this promising area contributes to the search for a solution to this problemOne of the main stages of recognition is classification. This article describes the results of handwritten signature recognition using a convolutional neural network. A database of handwritten signatures of 10 people was used for

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Epishkina, A. V., A. V. Beresneva, S. S. Babkin, A. S. Kurnev, and V. Yu Lermontov. "About handwritten signature verification." Prikladnaya diskretnaya matematika. Prilozhenie, no. 10 (September 1, 2017): 73–76. http://dx.doi.org/10.17223/2226308x/10/31.

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Reddy Polu, Omkar. "Deep Learning - Based Handwritten Signature Verification System." International Journal of Science and Research (IJSR) 13, no. 11 (2024): 1886–90. https://doi.org/10.21275/sr241114114304.

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Ubul, Kurban, Rayima Ablikim, Nurbiya Yadikar, and Mavjuda Zunun. "Non-Western Script Based Off-Line Handwritten Signature Technology: A Survey." Applied Mechanics and Materials 519-520 (February 2014): 606–10. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.606.

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Recognition and verification systems plays very critical role in the area of information security as they are very essential to user certification. In resent years, off-line signature recognition and verification receiving renewed interest and only one of several techniques used to verify the identities of individuals, also that one of the biometric techniques. Signatures offer a secure means for confirmation and authorization in legal documents. Thus, nowadays the signature identification and verification becomes an indispensable part for including embedded signatures of automating the rapid

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G V, Giridhar, and Vijayalaxmi S D. "An Innovative Approaches to Handwritten Signature Verification using CNN." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 07 (2025): 1–9. https://doi.org/10.55041/ijsrem51326.

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Handwritten signatures serve as a well known way for identity verification, delivering distinctive biometric feature for personal identification. The complexity and variety of signatures offer substantial obstacles in attaining reliable verification. This work proposes a unique technique to handwritten signature substantiation using CNNs. The purpose is to harness authority of vast knowledge to boost accuracy & reliability of signature verification system. CNNs are used due to their amazing capabilities in image processing & feature extraction. Proposed system adopts a Siamese network

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Guo, Yuhang, Siyuan Li, and Jinxuan Wu. "Research advanced in offline handwritten signature verification." Applied and Computational Engineering 6, no. 1 (2023): 1244–52. http://dx.doi.org/10.54254/2755-2721/6/20230653.

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Biometrics technology has penetrated into various fields of our lives that require authentication and verification, such as criminal investigation, check processing and legal procedures, which is a focus of debate in the scientific research community. As a biometric feature that is easy to obtain, verification of handwritten signatures has attracted great interest in the past several decades. Although the online handwritten signature verification (OnHSV) system can obtain more information and has a higher accuracy rate, the offline handwritten signature verification (OfHSV) system remains the

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Researcher. "MULTISCRIPT PATTERN RECOGNITION AND HANDWRITTEN SIGNATURE VERIFICATION SYSTEM FOR FORENSIC DOCUMENT EXAMINATION." International Journal of Advanced Research in Engineering and Technology (IJARET) 15, no. 4 (2024): 121–34. https://doi.org/10.5281/zenodo.13375479.

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Background: In the realm of forensic document examination, the accurate recognition and verification of handwritten signatures across multiple scripts present significant challenges. Multiscript pattern recognition is crucial for authenticating documents in diverse linguistic contexts. This study explores the development of a multiscript pattern recognition and handwritten signature verification system tailored for forensic applications. Methods: The proposed system integrates advanced image processing techniques, machine learning algorithms, and a multiscript database to analyze and verify ha

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SABOURIN, ROBERT, RÉJEAN PLAMONDON, and LOUIS BEAUMIER. "STRUCTURAL INTERPRETATION OF HANDWRITTEN SIGNATURE IMAGES." International Journal of Pattern Recognition and Artificial Intelligence 08, no. 03 (1994): 709–48. http://dx.doi.org/10.1142/s0218001494000383.

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The interpretation of handwritten signature images should be closely related to the writer’s identity. The representation and analysis of the handwritten signature is the major challenge in the field of automatic signature verification. A new concept of representation and interpretation of handwritten signature images is advocated. The segmentation process breaks up the signature into a collection of arbitrarily-shaped primitives. In the next step, a local interpretation process serves as a sophisticated template matching, permitting the labeling of all primitives from the test primitive set.

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Roszczewska, Katarzyna, and Ewa Niewiadomska-Szynkiewicz. "Online Signature Biometrics for Mobile Devices." Sensors 24, no. 11 (2024): 3524. http://dx.doi.org/10.3390/s24113524.

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This paper addresses issues concerning biometric authentication based on handwritten signatures. Our research aimed to check whether a handwritten signature acquired with a mobile device can effectively verify a user’s identity. We present a novel online signature verification method using coordinates of points and pressure values at each point collected with a mobile device. Convolutional neural networks are used for signature verification. In this paper, three neural network models are investigated, i.e., two self-made light SigNet and SigNetExt models and the VGG-16 model commonly used in i

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Tariq, Umair, Zonghai Hu, Rokham Tariq, Muhammad Shahid Iqbal, and Muhammad Sadiq. "High-Performance Embedded System for Offline Signature Verification Problem Using Machine Learning." Electronics 12, no. 5 (2023): 1243. http://dx.doi.org/10.3390/electronics12051243.

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This paper proposes a high-performance embedded system for offline Urdu handwritten signature verification. Though many signature datasets are publicly available in languages such as English, Latin, Chinese, Persian, Arabic, Hindi, and Bengali, no Urdu handwritten datasets were available in the literature. So, in this work, an Urdu handwritten signature dataset is created. The proposed embedded system is then used to distinguish genuine and forged signatures based on various features, such as length, pattern, and edges. The system consists of five steps: data acquisition, pre-processing, featu

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Chen, Mengqi, Jiawei Lin, Yongpan Zou, and Kaishun Wu. "Acoustic Sensing Based on Online Handwritten Signature Verification." Sensors 22, no. 23 (2022): 9343. http://dx.doi.org/10.3390/s22239343.

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Handwritten signatures are widely used for identity authorization. However, verifying handwritten signatures is cumbersome in practice due to the dependency on extra drawing tools such as a digitizer, and because the false acceptance of a forged signature can cause damage to property. Therefore, exploring a way to balance the security and user experiment of handwritten signatures is critical. In this paper, we propose a handheld signature verification scheme called SilentSign, which leverages acoustic sensors (i.e., microphone and speaker) in mobile devices. Compared to the previous online sig

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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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Shaik, Bushra, Jyothi Manohar Katikireddy, Vamsidhar Kambham, and K. Sravani. "Offline Signature Verification Using Image Processing." E3S Web of Conferences 391 (2023): 01074. http://dx.doi.org/10.1051/e3sconf/202339101074.

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A person’s signature is merely a handwritten sign that closely resembles his/her name, frequently stylized and distinctive, and that expresses the person’s identity, intent, and consent. Two types of verifications are present. They are online signature verification and offline signature verification. Generally, Offline Signature verification is less efficient and slower process compare to online verification when come to the situation having larger number of documents and files to verify with in less time. Over the years, many researchers have developed so many methods for signature verificati

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Abazid, Majd, Nesma Houmani, and Sonia Garcia-Salicetti. "Enhancing Security on Touch-Screen Sensors with Augmented Handwritten Signatures." Sensors 20, no. 3 (2020): 933. http://dx.doi.org/10.3390/s20030933.

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We aim at enhancing personal identity security on mobile touch-screen sensors by augmenting handwritten signatures with specific additional information at the enrollment phase. Our former works on several available and private data sets acquired on different sensors demonstrated that there are different categories of signatures that emerge automatically with clustering techniques, based on an entropy-based data quality measure. The behavior of such categories is totally different when confronted to automatic verification systems in terms of vulnerability to attacks. In this paper, we propose a

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Tahir, N. M., Adam N. Ausat, Usman I. Bature, Kamal A. Abubakar, and Ibrahim Gambo. "Off-line Handwritten Signature Verification System: Artificial Neural Network Approach." International Journal of Intelligent Systems and Applications 13, no. 1 (2021): 45–57. http://dx.doi.org/10.5815/ijisa.2021.01.04.

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Nowadays, it is evident that signature is commonly used for personal verification, this justifies the necessity for an Automatic Verification System (AVS). Based on the application, verification could either be achieved Offline or Online. An online system uses the signature’s dynamic information; such information is captured at the instant the signature is generated. An offline system, on the other hand, uses an image (the signature is scanned). In this paper, some set of simple shaped geometric features are used in achieving offline Verification of signatures. These features include Baseline

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Al-banhawy, Nehal Hamdy, Heba Mohsen, and Neveen Ghali. "SIGNATURE IDENTIFICATION AND VERIFICATION SYSTEMS: A COMPARATIVE STUDY ON THE ONLINE AND OFFLINE TECHNIQUES." Future Computing and Informatics Journal 5, no. 1 (2020): 28–45. http://dx.doi.org/10.54623/fue.fcij.5.1.3.

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Handwritten signature identification and verification has become an active area of research in recent years. Handwritten signature identification systems are used for identifying the user among all users enrolled in the system while handwritten signature verification systems are used for authenticating a user by comparing a specific signature with his signature that is stored in the system. This paper presents a review for commonly used methods for pre-processing, feature extraction and classification techniques in signature identification and verification systems, in addition to a comparison

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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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Abdulhussien, Ansam A., Mohammad F. Nasrudin, Saad M. Darwish, and Zaid Abdi Alkareem Alyasseri. "A Genetic Algorithm Based One Class Support Vector Machine Model for Arabic Skilled Forgery Signature Verification." Journal of Imaging 9, no. 4 (2023): 79. http://dx.doi.org/10.3390/jimaging9040079.

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Recently, signature verification systems have been widely adopted for verifying individuals based on their handwritten signatures, especially in forensic and commercial transactions. Generally, feature extraction and classification tremendously impact the accuracy of system authentication. Feature extraction is challenging for signature verification systems due to the diverse forms of signatures and sample circumstances. Current signature verification techniques demonstrate promising results in identifying genuine and forged signatures. However, the overall performance of skilled forgery detec

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Researcher. "INTEGRATED SYSTEM FOR FORENSIC DOCUMENT ANALYSIS: MULTISCRIPT RECOGNITION AND HANDWRITTEN SIGNATURE VERIFICATION." Journal of Computer Engineering and Technology (JCET) 7, no. 2 (2024): 1–16. https://doi.org/10.5281/zenodo.13737405.

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BACKGROUND: Forensic document analysis plays a crucial role in verifying the authenticity of signatures and recognizing handwritten content. Traditional methods often struggle with diverse handwriting styles and various script forms, necessitating the development of integrated systems that enhance the accuracy and efficiency of document examination. This study introduces a novel approach to forensic document analysis by integrating multiscript recognition and handwritten signature verification into a unified system.METHODS: The integrated system employs a quantitative experimental design utili

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Hashim, Zainab, Hanaa M. Ahmed, and Ahmed Hussein Alkhayyat. "A Comparative Study among Handwritten Signature Verification Methods Using Machine Learning Techniques." Scientific Programming 2022 (October 15, 2022): 1–17. http://dx.doi.org/10.1155/2022/8170424.

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Nowadays, the verification of handwritten signatures has become an effective research field in computer vision as well as machine learning. Signature verification is naturally formulated as a machine-learning task. This task is performed by determining if the signature is genuine or forged. Therefore, it is considered a two‐class classification issue. Since handwritten signatures are widely used in legal documents and financial transactions, it is important for researchers to select an efficient machine-learning technique for verifying these signatures and to avoid forgeries that may cause man

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Singhal, Manas, Manish Trikha, and Maitreyee Dutta. "Time Independent Signature Verification using Normalized Weighted Coefficients." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 6 (2016): 2658. http://dx.doi.org/10.11591/ijece.v6i6.10908.

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<p>Signature verification is one of the most widely accepted verification methods in use. The application of handwritten signatures includes the banker’s checks, the credit and debit cards issued by banks and various legal documents. The time factor plays an important role in the framing of signature of an individual person. Signatures can be classified as: offline signature verification and online signature verification. In this paper a time independent signature verification using normalized weighted coefficients is presented. If the signature defining parameters are updated regularly

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Singhal, Manas, Manish Trikha, and Maitreyee Dutta. "Time Independent Signature Verification using Normalized Weighted Coefficients." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 6 (2016): 2658. http://dx.doi.org/10.11591/ijece.v6i6.pp2658-2664.

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<p>Signature verification is one of the most widely accepted verification methods in use. The application of handwritten signatures includes the banker’s checks, the credit and debit cards issued by banks and various legal documents. The time factor plays an important role in the framing of signature of an individual person. Signatures can be classified as: offline signature verification and online signature verification. In this paper a time independent signature verification using normalized weighted coefficients is presented. If the signature defining parameters are updated regularly

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Iman, Subhi Mohammed, and Khalaf Hussien Maher. "Off-line handwritten signature recognition based on genetic algorithm and Euclidean distance." International Journal of Artificial Intelligence (IJ-AI) 12, no. 3 (2023): 1238–49. https://doi.org/10.11591/ijai. v12.i3.pp1238-1249.

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Biometric authentication is a technology that has become significant in the high level of personal identity security. This paper provides a signature recognition system. This paper provides a static signature recognition system (SSRS). We have classified the signature in two ways. The first method uses the genetic algorithm (GA), considering that the signature is the chromosome with 35 genes, and each feature is a gene. With applying the processes of the GA between chromosomes and the formation of generations in sequence until we reach the optimal solution by finding the chromosome closest to

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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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Yassen A. AlKarem, Wijdan, Eman Thabet Khalid, and Khawla H. Ali. "Handwritten Signature Verification Method Using Convolutional Neural Network." Iraqi Journal for Electrical and Electronic Engineering 20, no. 2 (2024): 77–84. http://dx.doi.org/10.37917/ijeee.20.2.7.

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Automatic signature verification methods play a significant role in providing a secure and authenticated handwritten signature in many applications, to prevent forgery problems, specifically institutions of finance, and transections of legal papers, etc. There are two types of handwritten signature verification methods: online verification (dynamic) and offline verification (static) methods. Besides, signature verification approaches can be categorized into two styles: writer dependent (WD), and writer independent (WI) styles. Offline signature verification methods demands a high representatio

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Hong, Dong-Jin, Won-Du Chang, and Eui-Young Cha. "Handwritten Signature Generation Using Denoising Diffusion Probabilistic Models with Auxiliary Classification Processes." Applied Sciences 14, no. 22 (2024): 10233. http://dx.doi.org/10.3390/app142210233.

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Automatic signature verification has been widely studied for authentication purposes in real life, but limited data availability still poses a significant challenge. To address this issue, we propose a method with a denoising diffusion probabilistic model (DDPM) to generate artificial signatures that closely resemble authentic ones. In the proposed method, we modified the noise prediction process of the DDPM to allow the generation of signatures specific to certain classes. We also employed an auxiliary classification process to ensure that the generated signatures closely resemble the origina

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Myat, Mon Kyaw, San Nwe San, and Myint Yee Myint. "ANN Based Handwritten Signature Recognition System." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 2094–97. https://doi.org/10.5281/zenodo.3591095.

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Handwritten Signature Veri cation HSV is an automated method of verifying a signature by capturing features about a signature's shape i.e., static features and the characteristics of how the person signs his her name in real time i.e., dynamic features . This system provides a method of handwritten signature recognition and verification using the shapes of the signatures, artificial neural network and neural network simulation tool. The shapes of signatures are used to find the features points for features extraction. Then the extracted features are trained by using artificial neural netwo

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Hazra, Abhisek, Shuvajit Maity, Barnali Pal, and Asok Bandyopadhyay. "Adversarial attacks in signature verification: a deep learning approach." Computer Science and Information Technologies 5, no. 3 (2024): 215–26. http://dx.doi.org/10.11591/csit.v5i3.p215-226.

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Handwritten signature recognition in forensic science is crucial for identity and document authentication. While serving as a legal representation of a person’s agreement or consent to the contents of a document, handwritten signatures de termine the authenticity of a document, identify forgeries, pinpoint the suspects and support other pieces of evidence like ink or document analysis. This work focuses on developing and evaluating a handwritten signature verification sys tem using a convolutional neural network (CNN) and emphasising the model’s efficacy using hand-crafted adversarial attacks.

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Albasu, F. B., and M. A. Al Akkad. "Exploiting Deep Learning Techniques for the Verification of Handwritten Signatures." Intellekt. Sist. Proizv. 21, no. 3 (2023): 27–39. http://dx.doi.org/10.22213/2410-9304-2023-3-27-39.

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Biometric featuresare common measures of identity verification where signaturesarethe most used type. The digital technology has given birth to new ways of biometric identification, such as fingerprints, iris and face recognition,while dealing with handwritten signatures is still a challenging task, because handwritten signatures are more prone to forgery than other means of verification due to issues like computer error, insufficient datasets, and loss of information. This work aims to develop a system that takes a signature image as its input and determines whether the signature is genuine w

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Zhou, Yiwen, Jianbin Zheng, Huacheng Hu, and Yizhen Wang. "Handwritten Signature Verification Method Based on Improved Combined Features." Applied Sciences 11, no. 13 (2021): 5867. http://dx.doi.org/10.3390/app11135867.

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As a behavior feature, handwritten signatures are widely used in financial and administrative institutions. The appearance of forged signatures will cause great property losses to customers. This paper proposes a handwritten signature verification method based on improved combined features. According to advanced smart pen technology, when writing a signature, offline images and online data of the signature can be obtained in real time. It is the first time to realize the combination of offline and online. We extract the static and dynamic features of the signature and verify them with support

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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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Griechisch, Erika, and Gábor Németh. "Offline Signature Verification based on Centerline Similarities." Journal of the American Society of Questioned Document Examiners 18, no. 2 (2015): 17–27. https://doi.org/10.69525/jasqde.219.

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Digital signatures are becoming increasingly common in author identification. However, handwritten signatures play an important role in different aspects of life like business and the banking sector. Offline signature verification methods analyze the images and shapes of the signatures. Several methods use skeleton that is a frequently used shape descriptor that summarizes the general form of objects. Here, we present an offline signature verification method which is based on similarity measures designed for a comparison of 2D skeleton-like shape features. The proposed method was evaluated on

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Abhisek, Hazra, Maity Shuvajit, Pal Barnali, and Bandyopadhyay Asok. "Adversarial attacks in signature verification: a deep learning approach." Computer Science and Information Technologies 5, no. 3 (2024): 215–26. https://doi.org/10.11591/csit.v5i3.pp215-226.

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Handwritten signature recognition in forensic science is crucial for identity and document authentication. While serving as a legal representation of a person’s agreement or consent to the contents of a document, handwritten signatures de termine the authenticity of a document, identify forgeries, pinpoint the suspects and support other pieces of evidence like ink or document analysis. This work focuses on developing and evaluating a handwritten signature verification sys tem using a convolutional neural network (CNN) and emphasising the model’s efficacy using hand-crafted adversar

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Dr. S. Gomathi alias Rohini and K. R. Amuruthavarshini Priya. "Offline Signature Verification System using Convolutional Neural Networks." International Journal of Linguistics Applied Psychology and Technology (IJLAPT) 2, no. 03(Mar) (2025): 37–46. https://doi.org/10.69889/ijlapt.v2i03(mar).105.

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The paper titled “Offline Signature Verification System Using Convolutional Neural Networks (CNN)” is developed to automate the process of verifying handwritten signatures, ensuring accuracy and security in authentication systems. This system was developed using Python, with Tensor Flow / Keras for deep learning model development and OpenCV for image preprocessing. The implementation integrates Canny Edge Detection, Gaussian Blur and Grayscale conversion to pre-process signature images, enhancing feature extraction for accurate classification. The system is designed to replace traditional manu

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Deka, Alpana, and Lipi B. Mahanta. "An Ensemble Based Offline Handwritten Signature Verification System." Statistics, Optimization & Information Computing 8, no. 4 (2020): 902–14. http://dx.doi.org/10.19139/soic-2310-5070-447.

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In the field of security and forgery prevention, handwritten signatures are the most widely recognized biometric since long and also most practical. Although handwritten signature verification systems are studied using both On-line and Off-line approaches, Off-line signature verification systems are more difficult to compare to On-line verification systems. This is due to the lack of dynamic information, viz. a database which constantly stores the latest signature of the person. In the paper an approach using ensemble methods are adopted to classify a signature as forgery or not. In proposed s

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Beresneva, Anastasia V., and Anna V. Epishkina. "Approaches to online handwritten signature verification." Bezopasnost informacionnyh tehnology 27, no. 2 (2020): 78–85. http://dx.doi.org/10.26583/bit.2020.2.06.

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Pansare, Ashwini, and Shalini Bhatia. "Handwritten Signature Verification using Neural Network." International Journal of Applied Information Systems 1, no. 2 (2012): 44–49. http://dx.doi.org/10.5120/ijais12-450114.

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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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Amit, Puri* Rabia Verma. "A REVIEW PAPER ON OFFLINE SIGNATURE RECOGNITION SYSTEM USING RADON TRANSFORM WITH GENETIC ALGORITHM." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 12 (2016): 162–69. https://doi.org/10.5281/zenodo.192539.

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Hand handwritten signatures are generally acknowledged as a method for archive confirmation, approval and individual check. For legitimateness most archives like bank checks, travel visas and scholastic declarations need to have approved handwritten signatures. In modern world where extortion is widespread, there is the requirement for a programmed HSV (Handwritten signature verification) framework to supplement visual confirmation. Automated signature verification is as vital as other programmed distinguishing proof frameworks; however they vary from different frameworks that depend on owners

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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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Lokare, Chinmay, Rachana Patil, Saloni Rane, Deepakkumar Kathirasen, and Yogita Mistry. "Offline handwritten signature verification using various Machine Learning Algorithms." ITM Web of Conferences 40 (2021): 03010. http://dx.doi.org/10.1051/itmconf/20214003010.

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In today’s world it is necessary to protect one’s authenticity in order to ensure the protection of personal information that only the authenticate credentials of a person can have access to. Nowadays there is an increase in number of malpractices like signature forgery to access the important information of a person. To encounter signature verification problem, there have been a number of advances in verifying the authenticity of signature using various techniques including Machine Learning and Deep Learning. This paper introduces a novel approach to verify the signatures using difference of

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Srilekha, Nalla. "Signature Recognition and Verification Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32231.

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This project focuses on the development and implementation of a robust signature recognition and verification system leveraging machine learning techniques. Handwritten signatures serve as essential personal identifiers in numerous applications, such as financial transactions, legal documents, and access control. Traditional methods of signature verification often lack efficiency and accuracy, prompting the need for automated and intelligent systems. The proposed project aims to address this challenge by employing state-of-the-art machine learning algorithms for signature analysis. The project

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Soe, Moe Myint, Moe Myint Moe, and Aye Cho Aye. "Handwritten Signature Verification System using Sobel Operator and KNN Classifier." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 1776–79. https://doi.org/10.5281/zenodo.3591463.

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Signature is one of the most widely accepted personal attributes for identity verification. Signature verification is a scheme to verify cheque for bank security. So, this system is proposed as the off line handwritten signature verification system for the bank cheque image processing. In any offline signature verification system, feature extraction stage is the most vital and difficult stage. In this system, sobel gradient operator is used to extract signature features. After extracting features, this system performs the verification process by using k nearest neighbor KNN classifier. This sy

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Putz-Leszczynska, Joanna. "The Influence of Template Ageing on a Dynamic Signature Verification System." Journal of Forensic Document Examination 24 (December 31, 2014): 47–52. http://dx.doi.org/10.31974/jfde24-47-52.

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This paper addresses template ageing in automatic signature verification systems. Handwritten signatures are a behavioral biometric sensitive to the passage of time. The experiments in this paper utilized a database that contains signature realizations captured in three sessions. The last session was captured seven years after the first one. The results presented in this paper show a potential risk of using an automatic handwriting verification system without including template ageing Purchase Article for $10

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Muhtar, Yusnur, Mahpirat Muhammat, Nurbiya Yadikar, Alimjan Aysa, and Kurban Ubul. "FC-ResNet: A Multilingual Handwritten Signature Verification Model Using an Improved ResNet with CBAM." Applied Sciences 13, no. 14 (2023): 8022. http://dx.doi.org/10.3390/app13148022.

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Offline signature verification is a widely used biometric method in finance, law, and administrative procedures. However, existing deep convolutional neural network models perform poorly on signature datasets that span different regions and ethnic people, while also suffering from problems such as large parameter counts and slow inference speeds. To address these issues, we propose an improved residual network model (FC-ResNet). This model introduces a convolutional block attention module into the classical residual network to adapt to the diversity and variability of signatures, while also co

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AMMAR, MAAN. "PROGRESS IN VERIFICATION OF SKILLFULLY SIMULATED HANDWRITTEN SIGNATURES." International Journal of Pattern Recognition and Artificial Intelligence 05, no. 01n02 (1991): 337–51. http://dx.doi.org/10.1142/s0218001491000193.

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This paper compares the performances of parametric and reference pattern based features (RPBFs) in the verification of skillfully simulated handwritten signatures. The comparison shows that RPBFs significantly improve results and give about 90% correct verification using only shape features. The performance of the used shape features is independent of the signature shape, language and position in the document. The careful analysis of the experimental results of using RPBFs in verification has led to the conclusion that two-dimensional RPBFs will give much better performance.

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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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