This dataset contains 12,500 meter images acquired in the field by the employees of the Energy Company of Paraná (Copel), which directly serves more than 4 million consuming units, across 395 cities and 1,113 locations (i.e., districts, villages and settlements), located in the Brazilian state of Paraná.

Copel-AMR is composed of images captured in unconstrained scenarios, which typically include blur (due to camera motion), dirt, scale variations, in-plane and out-of-plane rotations, reflections, shadows, and occlusions. In 2,500 images (i.e., 20% of the dataset), it is not even possible to perform the meter reading due to occlusions or faulty meters.

The images have a resolution of 480×640 or 640×480 pixels, depending on the orientation in which they were taken. Considering that the meter is operational and that there are no occlusions, these resolutions are enough for the meter reading to be legible.

The dataset was randomly split as follows: 5,000 images for training, 5,000 images for testing and 2,500 images for validation, following the split protocol (i.e., 40%/40%/20%) used in the UFPR-AMR dataset. For reproducibility purposes, the subsets generated are explicitly available along with the Copel-AMR dataset.

For each image in our dataset, we manually labeled the meter reading, the position (x, y) of each of the four corners of the counter, and a bounding box (x, y, w, h) for each digit. Corner annotations – which can be converted to a bounding box – enable the counter to be rectified, while bounding boxes enable the training of object detectors.