GAS meter reading from real world images using a multi-net system

We present a new approach for automatic gas meter reading from real world images. The gas meter reading is usually done on site by an operator and a picture is taken from a mobile device as proof of reading. Since the reading operation is prone to errors, the proof image is checked offline by another operator to confirm the reading. In this study, we present a method to support the validation process in order to reduce the human effort. Our approach is trained to detect and recognize the text of a particular area of interest. Firstly we detect the region of interest and segment the text contained using a method based on an ensemble of neural models. Then we perform an optical character recognition using a Support Vector Machine. We evaluated every step of our approach, as well as the overall assessment, showing that despite the complexity of the problem our method provide good results also when applied to degraded images and can therefore be used in real applications.

► Our method validates a human reading of a gas meter counter from a real image.
► No knowledge derived from specific meter models can help to localize the counter area.
► Using an ensemble of neural models we detect and segment the meter counting text.
► Exploiting a Fourier analysis we detect regions containing each counter digit.
► The method is able to validate correctly 87% of images using a test dataset.