American Journal of Neural Networks and Applications

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A Neural Network Approach to Writer’s Model for Full Likelihood Ratio in Handwriting Analysis

Received: 04 November 2020    Accepted: 02 December 2020    Published: 16 December 2020
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Abstract

Handwriting is an integral part of our life that can predict who we are because the style of writing is unique for every person. Handwriting is also a key element in document examination as it leaves a forensic document examiner with the task of determining who the writer of a particular document is and this is achieved through the likelihood ratio (LR) paradigm. Inability to model an individual’s handwriting over time has made estimating a full likelihood ratio for comparative handwriting analysis impossible thereby employing nuisance parameters and subjectivity in computation of LR that is not full. This research employed back propagation neural network (BPNN) to model the writing pattern of individuals with input layer as the features of handwriting characters, two hidden layers of three neurons each, activation function sigmoid (s) and an output handwriting. With the help of handwriting model for individual writers, little or no assumptions and no nuisance parameters were employed in achieving full likelihood ratio for comparative handwriting analysis in forensic science. From the research carried out, it can be concluded that modeling an individual’s handwriting is a crucial factor in achieving a full likelihood ratio, little/or no inconclusiveness in result reporting and a less degree of disagreements for handwriting identification in a forensic environment.

DOI 10.11648/j.ajnna.20200602.13
Published in American Journal of Neural Networks and Applications (Volume 6, Issue 2, December 2020)
Page(s) 29-35
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

LR, BPNN, Handwriting, Nuisance Parameter, Forensic Handwriting, Document Examination

References
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Author Information
  • Department of Computer Science & Information Technology, Bowen University, Iwo, Nigeria

  • Department of Computer Science, University of Ibadan, Ibadan, Nigeria

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  • APA Style

    Abiodun Adeyinka Oluwabusayo, Adeyemo Adesesan Barnabas. (2020). A Neural Network Approach to Writer’s Model for Full Likelihood Ratio in Handwriting Analysis. American Journal of Neural Networks and Applications, 6(2), 29-35. https://doi.org/10.11648/j.ajnna.20200602.13

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    Abiodun Adeyinka Oluwabusayo; Adeyemo Adesesan Barnabas. A Neural Network Approach to Writer’s Model for Full Likelihood Ratio in Handwriting Analysis. Am. J. Neural Netw. Appl. 2020, 6(2), 29-35. doi: 10.11648/j.ajnna.20200602.13

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    AMA Style

    Abiodun Adeyinka Oluwabusayo, Adeyemo Adesesan Barnabas. A Neural Network Approach to Writer’s Model for Full Likelihood Ratio in Handwriting Analysis. Am J Neural Netw Appl. 2020;6(2):29-35. doi: 10.11648/j.ajnna.20200602.13

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  • @article{10.11648/j.ajnna.20200602.13,
      author = {Abiodun Adeyinka Oluwabusayo and Adeyemo Adesesan Barnabas},
      title = {A Neural Network Approach to Writer’s Model for Full Likelihood Ratio in Handwriting Analysis},
      journal = {American Journal of Neural Networks and Applications},
      volume = {6},
      number = {2},
      pages = {29-35},
      doi = {10.11648/j.ajnna.20200602.13},
      url = {https://doi.org/10.11648/j.ajnna.20200602.13},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajnna.20200602.13},
      abstract = {Handwriting is an integral part of our life that can predict who we are because the style of writing is unique for every person. Handwriting is also a key element in document examination as it leaves a forensic document examiner with the task of determining who the writer of a particular document is and this is achieved through the likelihood ratio (LR) paradigm. Inability to model an individual’s handwriting over time has made estimating a full likelihood ratio for comparative handwriting analysis impossible thereby employing nuisance parameters and subjectivity in computation of LR that is not full. This research employed back propagation neural network (BPNN) to model the writing pattern of individuals with input layer as the features of handwriting characters, two hidden layers of three neurons each, activation function sigmoid (s) and an output handwriting. With the help of handwriting model for individual writers, little or no assumptions and no nuisance parameters were employed in achieving full likelihood ratio for comparative handwriting analysis in forensic science. From the research carried out, it can be concluded that modeling an individual’s handwriting is a crucial factor in achieving a full likelihood ratio, little/or no inconclusiveness in result reporting and a less degree of disagreements for handwriting identification in a forensic environment.},
     year = {2020}
    }
    

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    AU  - Abiodun Adeyinka Oluwabusayo
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    AB  - Handwriting is an integral part of our life that can predict who we are because the style of writing is unique for every person. Handwriting is also a key element in document examination as it leaves a forensic document examiner with the task of determining who the writer of a particular document is and this is achieved through the likelihood ratio (LR) paradigm. Inability to model an individual’s handwriting over time has made estimating a full likelihood ratio for comparative handwriting analysis impossible thereby employing nuisance parameters and subjectivity in computation of LR that is not full. This research employed back propagation neural network (BPNN) to model the writing pattern of individuals with input layer as the features of handwriting characters, two hidden layers of three neurons each, activation function sigmoid (s) and an output handwriting. With the help of handwriting model for individual writers, little or no assumptions and no nuisance parameters were employed in achieving full likelihood ratio for comparative handwriting analysis in forensic science. From the research carried out, it can be concluded that modeling an individual’s handwriting is a crucial factor in achieving a full likelihood ratio, little/or no inconclusiveness in result reporting and a less degree of disagreements for handwriting identification in a forensic environment.
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