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dc.contributor.authorAsiroğlu, Batuhan
dc.contributor.authorMete, Büşra Rümeysa
dc.contributor.authorYıldız, Eyyüp
dc.contributor.authorNalçakan, Yağız
dc.contributor.authorSezen, Alper
dc.contributor.authorDağtekin, Mustafa
dc.contributor.authorEnsari, Tolga
dc.date.accessioned2020-02-20T11:47:35Z
dc.date.available2020-02-20T11:47:35Z
dc.date.issued2019
dc.identifier.isbn978-1-7281-1013-4
dc.identifier.urihttps://hdl.handle.net/11413/6253
dc.description.abstractThe design cycle for a web site starts with creating mock-ups for individual web pages either by hand or using graphic design and specialized mock-up creation tools. The mock-up is then converted into structured HTML or similar markup code by software engineers. This process is usually repeated many more times until the desired template is created. In this study, our aim is to automate the code generation process from hand-drawn mock-ups. Hand drawn mock-ups are processed using computer vision techniques and subsequently some deep learning methods are used to implement the proposed system. Our system achieves 96% method accuracy and 73% validation accuracy.
dc.language.isoen_UStr_TR
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectObject Detection
dc.subjectObject Recognition
dc.subjectConvolutional Neural Network
dc.subjectDeep Learning
dc.subjectAutomatic Code Generation
dc.subjectHTML
dc.titleAutomatic HTML code generation from mock-up images using machine learning techniques
dc.typeBook chaptertr_TR
dc.relation.journal2019 Scientific Meeting on Electrical-Electronics Biomedical Engineering and Computer Science (EBBT)tr_TR
dc.identifier.scopus2-s2.0-85068579133


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Attribution-NonCommercial-NoDerivs 3.0 United States
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States