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Title: Ballot Tabulation Using Deep Learning
Currently deployed election systems that scan and process hand-marked ballots are not sophisticated enough to handle marks insufficiently filled in (e.g., partially filled-in), improper marks (e.g., using check marks or crosses instead of filling in bubbles), or marks outside of bubbles, other than setting a threshold to detect whether the pixels inside bubbles are dark and dense enough to be counted as a vote. The current works along this line are still largely limited by their degree of automation and require substantial manpower for annotation and adjudication. In this study, we propose a highly automated deep learning (DL) mark segmentation model-based ballot tabulation assistant able to accurately identify legitimate ballot marks. For comparison purposes, a highly customized traditional computer vision (T-CV) mark segmentation-based method has also been developed to compare with the DL-based tabulator, with a detailed discussion included. Our experiments conducted on two real election datasets achieved the highest accuracy of 99.984% on ballot tabulation. In order to further enhance our DL model’s capability of detecting the marks that are underrepresented in training datasets, e.g., insufficiently or improperly filled marks, we propose a Siamese network architecture that enables our DL model to exploit the contrasting features between a handmarked ballot image and its corresponding blank template image to detect marks. Without the need for extra data collection, by incorporating this novel network architecture, our DL modelbased tabulation method not only achieved a higher accuracy score but also substantially reduced the overall false negative rate.  more » « less
Award ID(s):
2154589
PAR ID:
10439532
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
2023 IEEE International Conference on Information Reuse
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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