  Automatic meter reading technology is not yet widespread. Gas, electricity,
or water accumulation meters reading is mostly done manually on-site either by
an operator or by the homeowner. In some countries, the operator takes a
picture as reading proof to confirm the reading by checking offline with
another operator and/or using it as evidence in case of conflicts or
complaints. The whole process is time-consuming, expensive, and prone to
errors. Automation can optimize and facilitate such labor-intensive and human
error-prone processes. With the recent advances in the fields of artificial
intelligence and computer vision, automatic meter reading systems are becoming
more viable than ever. Motivated by the recent advances in the field of
artificial intelligence and inspired by open-source open-access initiatives in
the research community, we introduce a novel large benchmark dataset of
real-life gas meter images, named the NRC-GAMMA dataset. The data were
collected from an Itron 400A diaphragm gas meter on January 20, 2020, between
00:05 am and 11:59 pm. We employed a systematic approach to label the images,
validate the labellings, and assure the quality of the annotations. The dataset
contains 28,883 images of the entire gas meter along with 57,766 cropped images
of the left and the right dial displays. We hope the NRC-GAMMA dataset helps
the research community to design and implement accurate, innovative,
intelligent, and reproducible automatic gas meter reading solutions.
