A comprehensive dataset for word-wheel water meter reading under challenging conditions

We present a comprehensive dataset designed for segmentation, recognition,
and classification tasks related to word-wheel type water meter reading.
This dataset encompasses a wide range of real-world scenarios, including
clear, blurry, reflective, and obstructed images, captured under various
environmental conditions. As a result, it provides a robust benchmark for
model training and evaluation. It contains over 50,000 water meter images,
annotated with segmentation masks, recognition labels, and multi-hot
encoded classification labels. These annotations facilitate the training
of models for segmentation, recognition, and multi-task classification,
enabling them to address various challenges. Technical validation
highlights the effectiveness and utility of the dataset in segmentation
and recognition tasks across various challenge scenarios.