  In this paper, we tackle Automatic Meter Reading (AMR) by leveraging the high
capability of Convolutional Neural Networks (CNNs). We design a two-stage
approach that employs the Fast-YOLO object detector for counter detection and
evaluates three different CNN-based approaches for counter recognition. In the
AMR literature, most datasets are not available to the research community since
the images belong to a service company. In this sense, we introduce a new
public dataset, called UFPR-AMR dataset, with 2,000 fully and manually
annotated images. This dataset is, to the best of our knowledge, three times
larger than the largest public dataset found in the literature and contains a
well-defined evaluation protocol to assist the development and evaluation of
AMR methods. Furthermore, we propose the use of a data augmentation technique
to generate a balanced training set with many more examples to train the CNN
models for counter recognition. In the proposed dataset, impressive results
were obtained and a detailed speed/accuracy trade-off evaluation of each model
was performed. In a public dataset, state-of-the-art results were achieved
using less than 200 images for training.
