UniDataPro/water-meters

Water Meters Dataset - OCR, Masks

Dataset comprises 5,000+ images of water meters, including their corresponding segmentation masks and** OCR labels** for meter readings. It is designed to facilitate research in water consumption analysis and smart meter technology, providing valuable insights into residential and commercial water usage.

By utilizing this dataset, researchers and developers can enhance their understanding of water meter readings and improve methods for automatic recognition and data collection. - Get the data

Example of the data

Each image in the dataset contains critical information such as the value of the water meter and the location of the bounding box, enabling accurate meter reading extraction. The dataset is organized into three distinct folders: bounding box images, original images, and segmentation masks, allowing for efficient data management and accessibility.

Frequently Asked Questions

What file formats are provided?

The water meter images are supplied in JPG format, while annotations are provided as CSV files. Using standard image and annotation formats makes the dataset easy to integrate with popular computer vision frameworks such as PyTorch, TensorFlow, Detectron2, and YOLO-based pipelines.

Who can benefit from this water meter dataset?

The dataset is valuable for utility providers, smart city developers, computer vision engineers, OCR researchers, IoT solution providers, environmental researchers, and organizations developing automated infrastructure inspection or meter reading solutions based on water meter images.

How was the water meter dataset collected?

The dataset was collected through crowdsourcing platforms, enabling the acquisition of diverse water meter images captured in real-world environments.

💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at https://unidata.pro to discuss your requirements and pricing options.

This dataset provides a solid foundation for developing advanced recognition algorithms and improving the accuracy of water meter reading systems, which are essential for effective water resource management and conservation efforts.

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