  Manually reading and logging gauge data is time inefficient, and the effort
increases according to the number of gauges available. We present a computer
vision pipeline that automates the reading of analog gauges. We propose a
two-stage CNN pipeline that identifies the key structural components of an
analog gauge and outputs an angular reading. To facilitate the training of our
approach, a synthetic dataset is generated thus obtaining a set of realistic
analog gauges with their corresponding annotation. To validate our proposal, an
additional real-world dataset was collected with 4.813 manually curated images.
When compared against state-of-the-art methodologies, our method shows a
significant improvement of 4.55 in the average error, which is a 52% relative
improvement. The resources for this project will be made available at:
https://github.com/fuankarion/automatic-gauge-reading.
