research-article

Segmenting Handwritten Signatures at Their Perceptually Important Points

Authors:

J. J. Brault,

R. PlamondonAuthors Info & Claims

IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 15, Issue 9

Pages 953 - 957

https://doi.org/10.1109/34.232079

Published: 01 September 1993 Publication History

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Abstract

A new algorithm for segmenting continuous handwritten signatures sampled by a digitizer is described. The segmentation points are found using a two-step procedure. The principal step is to construct a function that weights the perceptual importance of every signature point according to its specific neighboring points. The second step points out the various local maxima of this function that correspond to where the signature should be segmented. The method is well illustrated and tested on a number of signatures that require different kinds of segmentation decisions.

References

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{2} J. -J. Brault, "Proposition et vérification d'un coefficient de difficulté d'imitation des signatures manuscrites," Ph.D. thesis, Ecole Polytechnique de Montréal, Canada, 384 pages, Dec. 1988.

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{3} J. -J. Brault and R. Plamondon, "How to detect problematic signers for automatic signature verification," in Proc. 1989 Int. Carnahan Conf. Security Technology (Zurich), Oct. 1989, pp. 127-132.

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{4} J. -J. Brault and R. Plamondon, "A complexity measure of handwritten curves: Modeling of dynamic signature forgery," to be published in IEEE Trans. Syst. Man Cybern., vol. 23, no. 2, Mar./Apr. 1993.

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{11} R. Plamondon, "A model-based segmentation framework for computer processing of handwriting," in Proc. 11th Int. Conf. Patt. Recogn. (The Hague, The Netherlands), Sept. 1992, pp. 303-307.

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Segmenting Handwritten Signatures at Their Perceptually Important Points
1. Applied computing
  1. Document management and text processing
    1. Document capture
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning algorithms

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cover image IEEE Transactions on Pattern Analysis and Machine Intelligence

IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 15, Issue 9

September 1993

125 pages

ISSN:0162-8828

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IEEE Computer Society

United States

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Published: 01 September 1993

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Gensler ASick B(2018)Performing event detection in time series with SwiftEventPattern Analysis & Applications10.1007/s10044-017-0657-021:2(543-562)Online publication date: 1-May-2018
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Wenyin LLu TYajie YLiang SZhang R(2013)Online stroke segmentation by quick penalty‐based dynamic programmingIET Computer Vision10.1049/iet-cvi.2012.02967:5(311-319)Online publication date: 1-Oct-2013
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Zhang ZWang KWang Y(2011)A survey of on-line signature verificationProceedings of the 6th Chinese conference on Biometric recognition10.5555/2074627.2074649(141-149)Online publication date: 3-Dec-2011
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