Water Meter Reading Based on Text Recognition Techniques and Deep Learning - Bay Nguyen van; Anh Nguyen; Kiet Tran-Trung; Thien Ho Huong; Ha Duong Thi Hong; Hau Nguyen Trung; Vinh Truong Hoang (IEEE Access)
In an increasingly technology-driven and automated world, optimizing energy management efficiency is crucial. A key component of this optimization is the precise measurement of water consumption, which plays a vital role in the allocation of resources for both households and businesses. While automated water meters are available, their adoption remains limited, particularly in less developed regions where manual meter reading is still common. This manual process often leads to inaccuracies and inefficiencies. This research aims to automate water meter readings, particularly in challenging and diverse environmental conditions. We propose a structured two-step approach to Automated Meter Reading, involving the identification of the counter region followed by digit segmentation and recognition. Using Convolutional Neural Networks, particularly YOLOv8 for detecting the counter region, we evaluate six CNN-based techniques for digit segmentation and recognition: PP-OCRv3, Structure-Preserving Inner Offset Network, Transformer-based Optical Character Recognition, RobustScanner, Show-Attend-and-Read, and Convolutional Recurrent Neural Network.