The problem of reading values from analogue instruments using comptures vision methods is an old problem that has already been solved many times using various methods. A request to re-implement the algorithm led to us ask ourself two questions: how could the problem be solved using state-of-the-art approaches, and how well do these new approaches compare with the existing old ones.
In this thesis, three methods are presented, each trying to solve the problem of in a different way. The first method is just a re-implementation and upgrade of existing methods. However, the other two methods use artificial intelligence and are based on two different neural networks VGG-16 and Mask R-CNN. In addition to describing the methods, we also implement them and compare their results.
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