Proceedings ArticleDOI
Signature verification using shape descriptors and multiple neural networks
Mehdi Dehghan,Karim Faez,M. Fathi +2 more
- Vol. 1, pp 415-418
TLDR
This method is capable of verifying simple and skilled forgeries with a good performance and is based on signature shape descriptors such as the skeleton, upper and lower envelopes, and the high pressure region of the signatures.Abstract:
This paper presents an off-line signature verification system. The verification is based on signature shape descriptors such as the skeleton, upper and lower envelopes, and the high pressure region of the signatures. Multiple multilayer perceptron neural network modules cooperating in taking a verification decision via a fuzzy integral voter are used. This method is capable of verifying simple and skilled forgeries with a good performance.read more
Citations
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Journal ArticleDOI
Online and off-line handwriting recognition: a comprehensive survey
TL;DR: The nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms are described.
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Systems and methods for image recognition using graph-based pattern matching
TL;DR: In this article, a method for creating a modeling structure for classifying objects in an image comprises converting an image into digital image data; using a processor, simplifying the digital images; using the processor, isolating objects in the simplified image data, creating graphs of the isolated objects, the graphs comprising vertices and edges; and finally, converting the graphs into representative graph data structures.
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Systems and methods for biometric identification using handwriting recognition
TL;DR: This article converted characters and a writing sample into mathematical graphs and used optical character recognition (OCR) techniques to identify these features in the handwriting sample so that drafts from two different samples can be aligned to compare to determine if the feature in the writing sample correlate with each other.
Journal ArticleDOI
Forgery detection by local correspondence
TL;DR: This thesis approaches the off-line problem by establishing a local correspondence between a model and a questioned signature, and performs skilled forgery detection by examining the writer-dependent information embedded at the sub-stroke level and trying to capture unballistic motion and tremor information in each stroke segment, rather than as global statistics.
Patent
Systems and methods for capturing handwritten information using handwriting analysis
Gilles Houle,Ronny Bakker,Johan Willem Piere Berkhuysen,Malayappan Shridhar,James G. Mason,Katerina Blinova,Babur Nugmanov +6 more
TL;DR: In this paper, a method for analyzing and assessing documents using a writing profile for documents, such as a payment instrument, is described. But it is not defined in this paper.
References
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Journal ArticleDOI
Automatic signature verification: the state of the art—1989–1993
Franck Leclerc,Réjean Plamondon +1 more
TL;DR: This paper summarizes the activity from year 1989 to 1993 in automatic signature verification and reports on the different projects dealing with dynamic, static and neural network approaches.
Journal ArticleDOI
Combining multiple neural networks by fuzzy integral for robust classification
Sung-Bae Cho,Jong-Sung Kim +1 more
TL;DR: The authors propose a method for multinetwork combination based on the fuzzy integral that nonlinearly combines objective evidence, in the form of a fuzzy membership function, with subjective evaluation of the worth of the individual neural networks with respect to the decision.
Journal ArticleDOI
Off-line signature verification based on geometric feature extraction and neural network classification
Kai Huang,Hong Yan +1 more
TL;DR: The role of signature shape description and shape similarity measure is discussed in the context of signature recognition and verification and the proposed method allows definite training control and at the same time significantly reduces the number of enrollment samples required to achieve a good performance.
Journal ArticleDOI
Signature verification using multiple neural classifiers
Reena Bajaj,Santanu Chaudhury +1 more
TL;DR: Experimental results show that combination of the classifiers increases reliability of the recognition results and is the unique feature of this work.
Proceedings ArticleDOI
An automatic fuzzy neural network driven signature verification system
R.W. Zhou,C. Quek +1 more
TL;DR: The characteristics of the POPFNN, such as the learning ability, generalization ability, and high computational ability, make the signature verification system particularly powerful when verifying skilled forgeries.