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dc.contributor.authorVyaznikov, P.-
dc.contributor.authorKotilevets, I.-
dc.identifier.citationVyaznikov, P. Developing a Seq2Seq neural network using visual attention to transform mathematical expressions from images to LaTeX / P. Vyaznikov, I. Kotilevets // Nano-Desing, Tehnology, Computer Simulations=Нанопроектирование, технология, компьютерное моделирование (NDTCS-2021) : тезисы докладов XIX Международного симпозиума, Минск, 28-29 октября 2021 года / Белорусский государственный университет информатики и радиоэлектроники ; редкол.: В. А. Богуш [и др.]. – Минск, 2021. – P. 61–63.ru_RU
dc.description.abstractThe presented neural network with the seq2seq architecture and Attention mechanism successfully solves the im2latex problem, which is confirmed by the results of measuring metrics. Generated captions for images with equations are quite accurate and, in most cases, coincide with the real ones. Such a solution can be used in mathematical programs to automatically translate images into LaTeX and further solve and analyze the resulting equations or expressions. The scope of use can be expanded by training the network on images with handwritten equations. A similar technology is used in the PhotoMath application, however, it has low accuracy and a small set of supported mathematical symbols. The described solution is devoid of both problems.ru_RU
dc.publisherБГУИР, РБru_RU
dc.subjectматериалы конференцийru_RU
dc.subjectconference proceedingsru_RU
dc.subjectneural networkru_RU
dc.subjectimages to LaTeXru_RU
dc.titleDeveloping a Seq2Seq neural network using visual attention to transform mathematical expressions from images to LaTeXru_RU
Appears in Collections:NDTCS 2021

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