Aadhaar-Assisted Election Roll Quality Enhancement System (ERQES) (EDQS)

Accurate and well-maintained electoral rolls are essential for ensuring the fairness and integrity of democratic processes. Large-scale voter databases, however, frequently contain spelling inconsistencies, incomplete demographic fields, duplicate registrations, and formatting variations that reduce data reliability. To address these challenges, this research introduces the Electoral Data Quality System (EDQS)—a Python-based automated framework designed to clean, validate, and enrich voter records with improved precision. The system employs a Weighted Probabilistic Matching Algorithm that assigns different significance levels to key attributes such as Name, Address, and Date of Birth. This weighted scoring approach enables EDQS to detect near-duplicate entries that conventional deterministic matching often fails to recognize. Once high-confidence matches are identified, the system uses verified Aadhaar demographic information to automatically correct erroneous or inconsistent voter details, reducing human intervention and ensuring standardization. Experimental evaluation demonstrates that EDQS significantly outperforms traditional matching methods, reduces manual verification efforts, and minimizes opportunities for fraudulent or duplicate voting. The proposed approach therefore offers a scalable, transparent, and cost-effective solution for improving the overall quality of electoral rolls and strengthening public trust in the election system.