A deep learning approach for the comparison of handwritten documents using latent feature vectors
Date
2024-02
Authors
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Wiley Periodicals LLC
Abstract
Forensic questioned document examiners still largely rely on visual assessments and expert judgment to determine the provenance of a handwritten document. Here, we propose a novel approach to objectively compare two handwritten documents using a deep learning algorithm. First, we implement a bootstrapping technique to segment document data into smaller units, as a means to enhance the efficiency of the deep learning process. Next, we use a transfer learning algorithm to systematically extract document features. The unique characteristics of the document data are then represented as latent vectors. Finally, the similarity between two handwritten documents is quantified via the cosine similarity between the two latent vectors. We illustrate the use of the proposed method by implementing it on a variety of collections of handwritten documents with different attributes, and show that in most cases, we can accurately classify pairs of documents into same or different author categories.
Series Number
Journal Issue
Is Version Of
Versions
Series
Academic or Administrative Unit
Type
Article
Comments
This article is published as J. Kim, S. Park, and A. Carriquiry, A deep learning approach for the comparison of handwritten documents using latent feature vectors, Stat. Anal. Data Min.: ASA Data Sci. J. 17 (2024), e11660. https://doi.org/10.1002/sam.11660. © 2024 The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.