Oil&Gas meters processing with Artificial Intelligence and Computer Vision

The equipment prints data on round discs, which are scanned and sent to the system for subsequent reading and data processing. Thus it was necessary to find models and solutions that could work with such incoming data.

To overcome this challenge we have developed an algorithm that can unfold a round disk and convert the image into a rectangular shape to make it possible to trace and read the coordinates (x, y) of the fixed curves.

The equipment prints several curves indicators that characterize the conditions for resource extraction on one graph. Each curve has its own color. For some of them, the background color could overlap with the line color, so it was necessary to find a solution that would allow to highlight the curves on the graph, bypassing this extraneous “noise”.

We have done work on training the neural network with test materials, which could select curves of different colors on the incoming graph with sufficient accuracy, considering all the features and potential interference.

The customer has many partners around the world, who use different equipment. The problem arose while processing various data formats.

We have trained a neural network that, based on the characteristics studied, could conclude that the input data belonged to one or another client, and, depending on this, send it to the necessary data processor.

Another problem was related to handwriting recognition because of the human factors: excitement, haste, absent-mindedness, etc.

The problem was solved by searching for data regions. From a few regions (bounding box), an attempt was made to recognize handwritten data (dates and numbers) using a trained neural network based on Google Tesseract.