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Systematic Measurement of Marginal Mark Types on Voting Ballots

Published

Author(s)

Andrea V. Bajcsy, Ya-Shian Li-Baboud, Mary C. Brady

Abstract

The presence of marginal marks on voting ballots is a ubiquitous problem in voting systems and it is a common source of dispute during federal and state-level elections. As of today, marginal marks are neither clearly countable as votes or as non-votes by optical mark scanners. We aim to establish quantitative measurements of marginal marks in order to provide an objective classification of ballot-mark types and ultimately improve algorithms in mark scanners. By utilizing 800 publicly available manually-marked ballot image scans from the 2009 Humboldt County, California election, we established a set of unique image features that distinguish between votes, non-votes, and five marginal mark types (check-mark, cross, partially filled, overfilled, lightly filled). The image features are related to semantic labels through both unsupervised and supervised machine-learning methods. We demonstrate the feasibility of developing an automated and quantifiable set of custom features to improve marginal mark type detection accuracy between 4 and 8 percent depending on classification model as compared to off-the-shelf features.
Citation
NIST Interagency/Internal Report (NISTIR) -

Keywords

voting, marginal marks, supervised classification

Citation

Bajcsy, A. , Li-Baboud, Y. and Brady, M. (2015), Systematic Measurement of Marginal Mark Types on Voting Ballots, NIST Interagency/Internal Report (NISTIR), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.IR.8069 (Accessed January 26, 2022)
Created July 23, 2015, Updated October 12, 2021