Automated Meter Reading System

Abstract

Advancements in automation technology have transformed several industries, including utility management companies. These advanced systems offer improvements in their services regarding capability, accuracy, and performance. One example of improvements is in Automated Meter Reading (AMR) frameworks. Since traditional meter reading methods are time-consuming and prone to errors, this has proven to be a fruitful field for AMR deployment. These systems have replaced manual human interpretations with new calculating processes, like image processing and optical character recognition (OCR), for extracting accurate readings from electricity meters. This thesis describes a software-based solution that presents the plan, advancement, and assessment of an imaginative AMR framework to collect data from meters. This study explores challenges in the automation of utility meter readings, including design differences in meters, lighting conditions, and image distortion. This thesis solves these problems via advanced preprocessing techniques and adaptive recognition algorithms.