Introduction

During the 2020 Presidential Election in Georgia, we were given a unique chance to conduct a detailed comparison between the original machine count and a comprehensive manual recount. Over the past three years, I've developed a specialized software that analyzes ballots and the audit marks made by voting tabulators. This software identifies each voter's selections, creating what I call a 'vote fingerprint.'

We compare these fingerprints across the data from both the original count and the recount to check for consistency. If there are discrepancies - like additional occurrences of a fingerprint in one set - we meticulously search the other set to find a corresponding match. If no match is found, it suggests a possible issue such as a duplicated or missing ballot. This technology aims to ensure the integrity and accuracy of the electoral process.

In the realm of election data analysis, the precise interpretation and translation of voter intent from ballot images into a comprehensible and analyzable format is crucial. We will delve into the process of converting Cast Vote Records (CVRs) from ballot images into a structured and analyzable format. The focus here is on creating a unique representation of each voter's choices, referred to as a "ballot fingerprint," which is instrumental in understanding voting patterns and behaviors.

Data Source and Initial Processing

Our primary data source comprises ballot images from diverse systems. Each system's output necessitates a standardized approach to ensure consistency in data interpretation. The AuditMark, located on the third page of the multi-page TIF ballot images, is pivotal in this process. It acts as a bridge between the physical, hand-marked paper ballots and the digital interpretation by the tabulator, which is programmed to recognize each race, candidate, and corresponding bubble placement.