Please use this identifier to cite or link to this item: http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/9933
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dc.contributor.authorTripathi, Samanvaya-
dc.contributor.authorJindal, Himanshu [Guided by]-
dc.date.accessioned2023-09-12T12:23:57Z-
dc.date.available2023-09-12T12:23:57Z-
dc.date.issued2023-
dc.identifier.urihttp://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/9933-
dc.descriptionEnrolment No. 191284en_US
dc.description.abstractHandwritten mathematical expressions are a significant part of many research fields, consisting of engineering, education, and science. The prevalent availability of powerful computational touch-screen appliances, like the modern emergence of deep neural networks as high-quality sequence recognition models, result in the widespread adoption of online recognition of handwritten mathematical expressions. A deeper study and improvement of such technologies is necessary to address the current challenges posed by the extensive usage of distance learning, and remote work due to the world pandemic.en_US
dc.language.isoen_USen_US
dc.publisherJaypee University of Information Technology, Solan, H.P.en_US
dc.subjectConvolutional neural networksen_US
dc.subjectMathematical expressionsen_US
dc.titleHandwritten Mathematical Expression Recognitionen_US
dc.typeProject Reporten_US
Appears in Collections:B.Tech. Project Reports

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