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oai:ebyu-rdlopen.rawdatalibrary.net:ebyu/139402026-10-18T20:43:07Zopenairedrivercom_ebyu_10000col_ebyu_10001

Automatic HTML code generation from mock-up images using machine learning techniquesAşiroǧlu, BatuhanMete, Büşta RümeysaYildiz, EyyüpNalçakan, YaǧizSezen, AlperDaǧtekin, MustafaEnsari, TolgaAutomatic code generationConvolutional neural networkDeep learningHTMLObject detectionObject recognitionBiomedical engineeringCodes (symbols)Deep neural networksElectronic medical equipmentMachine learningMockupsNeural networksWebsitesAutomatic code generationsCode GenerationComputer vision techniquesDesign cycleGraphic designLearning methodsMachine learning techniquesAutomatic programmingThe 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 mockup 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. © 2019 IEEE.Institute of Electrical and Electronics Engineers Inc.2019Conference Objecthttps://rdlopen.rawdatalibrary.net/r/ebyu/13940https://doi.org/10.1109/ebbt.2019.8741736978-172811013-4en2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019info:eu-repo/semantics/openAccessinfo:eu-repo/semantics/conferenceObjecthttps://ebyu-rdlopen.rawdatalibrary.net/tr/items/ebyu/13940