  Existing approaches for image-based Automatic Meter Reading (AMR) have been
evaluated on images captured in well-controlled scenarios. However, real-world
meter reading presents unconstrained scenarios that are way more challenging
due to dirt, various lighting conditions, scale variations, in-plane and
out-of-plane rotations, among other factors. In this work, we present an
end-to-end approach for AMR focusing on unconstrained scenarios. Our main
contribution is the insertion of a new stage in the AMR pipeline, called corner
detection and counter classification, which enables the counter region to be
rectified -- as well as the rejection of illegible/faulty meters -- prior to
the recognition stage. We also introduce a publicly available dataset, called
Copel-AMR, that contains 12,500 meter images acquired in the field by the
service company's employees themselves, including 2,500 images of faulty meters
or cases where the reading is illegible due to occlusions. Experimental
evaluation demonstrates that the proposed system, which has three networks
operating in a cascaded mode, outperforms all baselines in terms of recognition
rate while still being quite efficient. Moreover, as very few reading errors
are tolerated in real-world applications, we show that our AMR system achieves
impressive recognition rates (i.e., > 99%) when rejecting readings made with
lower confidence values.
