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Offline handwritten signature verification — Literature review [PDF]
2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), 2017 The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor).
Luiz G. Hafemann +2 more
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Offline Handwritten Signature Recognition
, 2011 {"references": ["G.A. Khuwaja. An adaptive combined classifier system for invariant\nface recognition. Digital Signal Processing 12, 21-46,\nDOI:10.1006/dspr.2001.0413, 2002.", "D. Zhang, J. Campbell, D. Maltoni, and R. Bolle. Special issue on\nbiometric systems. IEEE Trans. Systems, Man and Cybernetics - C,\n35(3):273-275, 2005.", "K.
Gulzar A. Khuwaja, Mohammad S. Laghari
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Graph Embedding for Offline Handwritten Signature Verification [PDF]
Proceedings of the 2019 3rd International Conference on Biometric Engineering and Applications, 2019 Due to the high availability and applicability, handwritten signatures are an eminent biometric authentication measure in our life. To mitigate the risk of a potential misuse, automatic signature verification tries to distinguish between genuine and forged signatures.
Michael Stauffer +3 more
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Enhanced Scheme For Handwritten Offline Signature Verification
, 2016 Handwritten Signature Verification is a b road area. It has been broadly researched in the last decades but there is an open research problem. There are so me possibilities to improve the results. To achieve better results we propose a new handwritten offline signature system.
Mrs. Rutuja M. Shinde, +1 more
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Offline Handwritten Signature Identification Using Adaptive Window Positioning Techniques
Signal & Image Processing : An International Journal, 2014 13 pages, 9 figures, 2 tables, Offline Handwritten Signature, GPDS dataset, Verification, Identification, Adaptive window ...
Sulong, Ghazali +2 more
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A preliminary study on biometric watermarking for offline handwritten signature
2007 IEEE International Conference on Telecommunications and Malaysia International Conference on Communications, 2007 This paper presents a preliminary study on biometric watermarking by using offline handwritten signature. Specifically, offline handwritten signature is discretized into binary bit string as hidden biometric watermark. It is reconstructed subsequently with the intention to authenticate the claimed source of the digital document. In this paper, we focus
C. Y. Low, Andrew B. J. Teoh, Connie Tee
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Intrapersonal Parameter Optimization for Offline Handwritten Signature Augmentation [PDF]
IEEE Transactions on Information Forensics and Security, 2021 Usually, in a real-world scenario, few signature samples are available to train an automatic signature verification system (ASVS). However, such systems do indeed need a lot of signatures to achieve an acceptable performance. Neuromotor signature duplication methods and feature space augmentation methods may be used to meet the need for an increase in ...
Teruo M. Maruyama +3 more
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Performance of hidden Markov model and dynamic Bayesian network classifiers on handwritten Arabic word recognition [PDF]
, 2011 This paper presents a comparative study of two machine learning techniques for recognizing handwritten Arabic words, where hidden Markov models (HMMs) and dynamic Bayesian networks (DBNs) were evaluated.
Alkhateeb, Jawad H. +3 more
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Component-based Segmentation of words from handwritten Arabic text [PDF]
, 2009 Efficient preprocessing is very essential for automatic recognition of handwritten documents. In this paper, techniques on segmenting words in handwritten Arabic text are presented.
AlKhateeb, J. H. +3 more
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Offline Handwritten Text Recognition and Signature Verification [PDF]
, 2021 Offline Handwritten Text Recognition (HTR) is one of the most interesting challenges in today's date in the field of Image processing. This paper introduces a novel technique to recognize the handwritten text by using Convolutional Recurrent Neural Network along with Connectionist Temporal Classification.
Rehaan Sajjad Arai +4 more
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