Image analysis of physical quantities dependencies graphs

Physical effects used at the conceptual design stage, described in “primary” sources of information, such as patents, often contain images of dependency graphs linking physical input and output quantities. Analysis of this information and its use to expand the description of a physical effect is a relevant task. The development of a method for analyzing graphic images for classifying dependency graphs of input and output physical quantities is described. This requires forming a labeled array of dependency graphs, as well as conducting computational experiments to identify the most effective architectures of neural network models. Algorithms for segmenting images of dependency graphs have been developed, allowing one to get rid of noisy (for the classification task) parts of the figure, such as coordinate axes, their designations, coordinate grids, etc.), the effectiveness of the OpenCV and scikit-image libraries has been tested on solving this problem. The formed labeled array contains more than 26 thousand images of dependency graphs. An algorithm for clustering images of dependency graphs by 9 classes (concave increase, concave decrease, convex increase, convex decrease, linear increase, linear decrease, constancy, jump increase, jump-like decrease) has been developed and implemented in software. Based on the results of the work, it can be concluded that all 3 methods of image clustering (LSTM, CNN and ViT) show almost the same results on the test dataset: Accuracy, Precision, Recall, F1-Score, AUC-ROC – 98%. At the same time, on arbitrary images from the patent array, the accuracy of the analysis decreases: for the LSTM and ViT methods by about 10%, and for CNN by about 2%.