Relevant bibliographies by topics / Handwritten Signature Verification

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Handwritten Signature Verification'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Handwritten Signature Verification"

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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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Dissertations / Theses on the topic "Handwritten Signature Verification"

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Sindle, Colin. "Handwritten signature verification using hidden Markov models." Thesis, Stellenbosch : Stellenbosch University, 2003. http://hdl.handle.net/10019.1/53445.

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Thesis (MScEng)--University of Stellenbosch, 2003.<br>ENGLISH ABSTRACT: Handwritten signatures are provided extensively to verify identity for all types of transactions and documents. However, they are very rarely actually verified. This is because of the high cost of training and employing enough human operators (who are still fallible) to cope with the demand. They are a very well known, yet under-utilised biometric currently performing far below their potential. We present an on-line/dynamic handwritten signature verification system based on Hidden Markov Models, that far out performs
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Le, Riche Pierre (Pierre Jacques). "Handwritten signature verification : a hidden Markov model approach." Thesis, Stellenbosch : Stellenbosch University, 2000. http://hdl.handle.net/10019.1/51784.

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Thesis (MEng)--University of Stellenbosch, 2000.<br>ENGLISH ABSTRACT: Handwritten signature verification (HSV) is the process through which handwritten signatures are analysed in an attempt to determine whether the person who made the signature is who he claims to be. Banks and other financial institutions lose billions of rands annually to cheque fraud and other crimes that are preventable with the aid of good signature verification techniques. Unfortunately, the volume of cheques that are processed precludes a thorough HSV process done in the traditional manner by human operators. It
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Amsbury, Burl. "Core technology through enterprise launch : a case study of handwritten signature verification." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/88338.

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Shashidhar, Sanda, and Amirisetti Sravya. "Online Handwritten Signature Verification System : using Gaussian Mixture Model and Longest Common Sub-Sequences." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15807.

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McCormack, Daniel Keith Raymond. "An investigation into the representation of data for the neural implementation of a handwritten static signature verification system." Thesis, Cardiff University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.338970.

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Olander, Sahlén Simon. "Feature Analysis in Online Signature Verification on Digital Whiteboard : An analysis on the performance of handwritten signature authentication using local and global features with Hidden Markov models." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-224661.

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The usage of signatures for authentication is widely accepted, and remains one of the most familiar biometric in our society. Efforts to digitalise and automate the verification of these signatures are hot topics in the field of Machine Learning, and a plethora of different tools and methods have been developed and adapted for this purpose. The intention of this report is to study the authentication of handwritten signatures on digital whiteboards, and how to most effectively set up a dual verification system based on Hidden Markov models (HMMs) and global aggregate features such as average sp
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Fang, Bin, and 房斌. "Verification of off-line handwritten signatures." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31241645.

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Kaplani, Eleni. "Human and computer-based verification of handwritten signatures." Thesis, University of Kent, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.396378.

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Qi, Yingyong. "A multiresolution approach to computer verification of handwritten signatures." Diss., The University of Arizona, 1993. http://hdl.handle.net/10150/186548.

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This dissertation presents procedures and results of works on computer signature verification. Two methods were developed and evaluated. First, verification was made using multi-resolution feature representation. This multi-resolution feature representation included global geometric characteristics and wavelet transformations of a signature image. A number of algorithms were developed to extract the global geometric features. A vector quantization classifier and a neural-network classifier were designed to use the multi-resolution representation for verification. Second, verification was made
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Lo, Wei-Hsien, and 羅尉賢. "Video-based Handwritten Signature Verification." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/68968926979823245638.

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碩士<br>國立中央大學<br>資訊工程研究所<br>98<br>This paper proposes a video-based handwritten signature verification framework. When acquiring signature information, we use a webcam in substitution for a digitizing tablet. Because webcams are more prevalent and cheaper than digitizing tablets, using webcams as sensors can reduce the cost. In addition, the features extracted using a webcam also contain more information. In tradition handwritten signature verification, features extracted using a digitizing tablet are mainly trajectories. But for the features extracted using a webcam, we can acquire pen graspin
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Book chapters on the topic "Handwritten Signature Verification"

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Garcia-Salicetti, Sonia, Nesma Houmani, Bao Ly-Van, et al. "Online Handwritten Signature Verification." In Guide to Biometric Reference Systems and Performance Evaluation. Springer London, 2009. http://dx.doi.org/10.1007/978-1-84800-292-0_6.

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Nouboud, Fathallah. "Handwritten Signature Verification: A Global Approach." In Fundamentals in Handwriting Recognition. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-78646-4_27.

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Kumar, Santosh, Shivani Mishra, Siddharth Gautam, and Bharat Bhushan. "Handwritten Signature Verification System Using IoT." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9927-9_61.

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Viriri, Serestina, and Jules-R. Tapamo. "Signature Verification Based on Handwritten Text Recognition." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10546-3_13.

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Wirotius, M., J. Y. Ramel, and N. Vincent. "Improving DTW for Online Handwritten Signature Verification." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30126-4_95.

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Pandey, Ashutosh, Vivek Srivastava, Shashank Yadav, Naveen Tiwari, B. D. K. Patro, and Abhishek Bajpai. "Handwritten Signature Detection and Verification Using CNN." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1923-5_29.

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Kumar, Ashok, and Karamjit Bhatia. "Offline Handwritten Signature Verification Using Decision Tree." In Cyber Technologies and Emerging Sciences. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2538-2_30.

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Lech, Michał, and Andrzej Czyżewski. "Handwritten Signature Verification System Employing Wireless Biometric Pen." In Studies in Big Data. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77604-0_22.

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Igarza, Juan J., Iñaki Goirizelaia, Koldo Espinosa, Inmaculada Hernáez, Raúl Méndez, and Jon Sánchez. "Online Handwritten Signature Verification Using Hidden Markov Models." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24586-5_48.

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Paudel, Nilakantha, Marco Querini, and Giuseppe F. Italiano. "Online Handwritten Signature Verification for Low-End Devices." In Communications in Computer and Information Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54433-5_3.

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Conference papers on the topic "Handwritten Signature Verification"

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Lanjewar, Rutuja Nitin, Radha Wasudeo Wande, and Swapnil Gundewar. "Handwritten Signature Verification using CNN." In 2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI). IEEE, 2024. https://doi.org/10.1109/idicaiei61867.2024.10842708.

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Xiao, Wanghui. "Offline Handwritten Signature Verification using Siamese Network Model." In 2024 6th International Conference on Electronic Engineering and Informatics (EEI). IEEE, 2024. http://dx.doi.org/10.1109/eei63073.2024.10696305.

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Akinrolabu, Olatunde David, Olusola Olajide Ajayi, Akinola Elijah Ebitigha, Adewuyi Adetayo Adegbite, Joy Rotimi Obafemi, and Jacob Kehinde Ogunleye. "Handwritten Signature Verification Model Using Transfer Deep Learning Technique." In 2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG). IEEE, 2024. http://dx.doi.org/10.1109/seb4sdg60871.2024.10629731.

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Reddy, Byreddy Sudhakara, Kanchupati Lavanya, Kummari Vamsi, Kallam Sai Seshi Reddy, and Maila Guru Lingaraju. "A Robust Approach for Handwritten Signature Verification Through Deep Learning." In 2025 7th International Conference on Intelligent Sustainable Systems (ICISS). IEEE, 2025. https://doi.org/10.1109/iciss63372.2025.11076424.

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Akter, Tahmina, Mst Sharmin Akter, Tanjim Mahmud, Rishita Chakma, Mohammad Shahadat Hossain, and Karl Andersson. "Evaluating the Performance of Machine Learning Models in Handwritten Signature Verification." In 2024 Asia Pacific Conference on Innovation in Technology (APCIT). IEEE, 2024. http://dx.doi.org/10.1109/apcit62007.2024.10673648.

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Kumar, G. Manoj, P. Satyanarayana, B. Sridhar, K. Tejo Rohith, G. V. S. Padma Rao, and V. Gokula Krishnan. "Deep Neural Networks based Handwritten Signature Verification using Machine Learning Algorithms." In 2024 Asian Conference on Intelligent Technologies (ACOIT). IEEE, 2024. https://doi.org/10.1109/acoit62457.2024.10939870.

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Vamsikrishna, M., Srinivasa Rao Bogireddy, Amit Gangopadhyay, Nilamadhab Mishra, Ajith Sundaram, and Priya Sharma. "Investigating Writer-Independent Deep Learning Techniques for Offline Handwritten Signature Verification." In 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N). IEEE, 2024. https://doi.org/10.1109/icac2n63387.2024.10895553.

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Ritika and Dalip. "A Comparative Analysis of Machine Learning Based Frameworks for Handwritten Signature Verification." In 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2024. http://dx.doi.org/10.1109/icesc60852.2024.10689878.

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Ren, Huaping, Heng Luo, Jingbo Fu, Chunyu Yan, and Zhangjian Wang. "Handwritten Signature Verification for Inspection Report of Special Equipment Based on Improved GANomaly." In 2024 6th International Conference on Electronics and Communication, Network and Computer Technology (ECNCT). IEEE, 2024. http://dx.doi.org/10.1109/ecnct63103.2024.10704488.

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Luan, Fangjun, Lu Cheng, and Shuai Yuan. "Writer-independent online handwritten signature verification using stable segments via an autoencoder-based Siamese network." In Seventeenth International Conference on Digital Image Processing (ICDIP 2025), edited by Xudong Jiang, Jindong Tian, Ting-Chung Poon, and Zhaohui Wang. SPIE, 2025. https://doi.org/10.1117/12.3073716.

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