Muhammad Ibrahim Chhipa, Hassan Berry, Fahad Chauhan and Abdul Muqsit Abbasiand Eman Ahmed, BCT Consultants, United Kingdom

This research paper explores the modernization of meter reading processes in third-world countries, with a specific focus on Pakistan. Traditional manual meter reading practices in these regions are labour-intensive, error-prone, and time-consuming, leading to suboptimal utility management and financial losses. To address these challenges, our study introduces a digitalized meter reading system enhanced by computer vision and machine learning technologies. This system automates data collection, enables real-time monitoring, and employs data analytics to enhance accuracy and efficiency. By reducing human error and ensuring timely data transmission, this digitized assistant empowers utility providers to make informed decisions and optimize resource allocation. Using Pakistan as a case study, we evaluate the impact of the digitized meter reading assistant on operational efficiency, cost-effectiveness, and overall utility management. Through key performance indicators and case studies, we demonstrate how computer vision and machine learning can enhance service delivery, reduce financial losses, and promote sustainability in third-world economies. This research contributes to the discourse on technological interventions in developing countries by highlighting the potential of digitizing essential services like meter reading. The findings offer valuable insights for policymakers, utility providers, and researchers seeking innovative solutions to address operational challenges in similar socio-economic contexts.

OCR, Low-cost, Edge Deployment, Angle invariant, Light invariant