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The problems of estimating the similarity index of mathematical and otherscientific publications containing equations and formulas are discussed for thefirst time. It is shown that the presence of equations and formulas (as well asfigures, drawings, and tables) is a complicating factor that significantlycomplicates the study of such texts. It is shown that the method fordetermining the similarity index of publications, based on taking into accountindividual mathematical symbols and parts of equations and formulas, isineffective and can lead to erroneous and even completely absurd conclusions.The possibilities of the most popular software system iThenticate, currentlyused in scientific journals, are investigated for detecting plagiarism andself-plagiarism. The results of processing by the iThenticate system ofspecific examples and special test problems containing equations (PDEs andODEs), exact solutions, and some formulas are presented. It has beenestablished that this software system when analyzing inhomogeneous texts, isoften unable to distinguish self-plagiarism from pseudo-self-plagiarism (falseself-plagiarism). A model complex situation is considered, in which theidentification of self-plagiarism requires the involvement of highly qualifiedspecialists of a narrow profile. Various ways to improve the work of softwaresystems for comparing inhomogeneous texts are proposed. This article will beuseful to researchers and university teachers in mathematics, physics, andengineering sciences, programmers dealing with problems in image recognitionand research topics of digital image processing, as well as a wide range ofreaders who are interested in issues of plagiarism and self-plagiarism.

The similarity index of scientific publications with equations and  formulas, identification of self-plagiarism, and testing of the iThenticate  system