Revitalizing Electoral Trust: Enhancing Transparency and Efficiency through Automated Voter Counting with Machine Learning

TL;DR

This paper explores the use of machine learning and image processing techniques to automate voter counting, aiming to improve transparency, efficiency, and public trust in elections.

Contribution

It introduces a novel automated voter counting system utilizing OpenCV, CVZone, and MOG2, with rigorous accuracy metrics for electoral applications.

Findings

Automated counting improves election efficiency.

Automated systems can rebuild public trust.

F1 score effectively compares manual and automated methods.

Abstract

In order to address issues with manual vote counting during election procedures, this study intends to examine the viability of using advanced image processing techniques for automated voter counting. The study aims to shed light on how automated systems that utilize cutting-edge technologies like OpenCV, CVZone, and the MOG2 algorithm could greatly increase the effectiveness and openness of electoral operations. The empirical findings demonstrate how automated voter counting can enhance voting processes and rebuild public confidence in election outcomes, particularly in places where trust is low. The study also emphasizes how rigorous metrics, such as the F1 score, should be used to systematically compare the accuracy of automated systems against manual counting methods. This methodology enables a detailed comprehension of the differences in performance between automated and human counting techniques by providing a nuanced assessment. The incorporation of said measures serves to reinforce an extensive assessment structure, guaranteeing the legitimacy and dependability of automated voting systems inside the electoral sphere.