A Neural Network Approach to Writer’s Model for Full Likelihood Ratio in Handwriting Analysis
American Journal of Neural Networks and Applications
Volume 6, Issue 2, December 2020, Pages: 29-35
Received: Nov. 4, 2020;
Accepted: Dec. 2, 2020;
Published: Dec. 16, 2020
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Abiodun Adeyinka Oluwabusayo, Department of Computer Science & Information Technology, Bowen University, Iwo, Nigeria
Adeyemo Adesesan Barnabas, Department of Computer Science, University of Ibadan, Ibadan, Nigeria
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.
Abiodun Adeyinka Oluwabusayo,
Adeyemo Adesesan Barnabas,
A Neural Network Approach to Writer’s Model for Full Likelihood Ratio in Handwriting Analysis, American Journal of Neural Networks and Applications.
Vol. 6, No. 2,
2020, pp. 29-35.
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