Abstract
A novel algorithm using computer vision and machine learning techniques have been developed in this research and applied to automate the reading of analog meters. This approach does not rely on any prior information about the meter being read or any human intervention during the process. High-level features of the meter including the graduation values and angles are extracted using a cascade of image contour filters with a series of digit classifiers. The features are refined and used to train regression models that return the reading of the analogue meter automatically. The proposed approach was tested to read a variety of offline and live-feed images of analog pointer meters automatically without any prior information about the meters.
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