A way to improve the quality of graphical data digitising

This paper considers the actual problem of graphical information converting back into raw data format that was used to represent it. This is due to the great scientifi achievements contained in editions from the Soviet period, as well as the intention global publishers from "unfriendly" countries to close access for Russian and Belarusian organisations to scientific publications and technical information databases. As a result, Russian scientists can only have graphical materials in formats similar to PDF documents. This paper considers a fairly simple way of solving this problem when digitising graphs in printed or electronic publications with low resolution and a large picture scale, which does not allow to detail its separate fragments. The procedure for pre-processing the original image in bitmap format is described. In order to improve the numerical data resulting accuracy from the subsequent digitising, a number of features are recommended, which are available in the well-known and most common graphical editors. These functions include changing the color mode of the image, color inversion, sharpening and contrast, linear scaling (vertical and horizontal scaling), and graph line spline approximation. The above operations are accessible to users with the minimum familiarity of graphical editors Microsoft Power Point and Adobe Photoshop. As an developed procedure use example, the results of FRB 160317 signal digitising (a so-called Fast Radio Burst), are presented. The digitising of its graphical image has provided more accurate additional information on signal characteristics not given by publication authors. A visual evaluation of the digitised FRB 160317 signal accuracy by matching it with the original graphical image is presented, which showed a satisfactory match to the original. The numerical data array obtained by digitising the raw graphical material using a pre-processing procedure is becomes available for further analysis. The described way can be used by university teachers both at the initial stages of students teaching to work with images and carry out data analysis, and when preparing teaching materials when organising the educational process using distance learning technologies. The results are applicable at the starting stages of scientifi research in the initial data set formation for dependency analysis in various subject areas, where there are no initial samples refl the results of observation.