Automatic HTML code generation from mock-up images using machine learning techniques

Conference ObjectAçık Erişim

Özet

The 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.

Anahtar Kelimeler

RDL Kalıcı Bağlantı (PID)
https://rdlopen.rawdatalibrary.net/r/ebyu/13940Resmi atıflarda kullanılan kalıcı adres
DOI
https://doi.org/10.1109/ebbt.2019.8741736Yayıncı resmi DOI adresi
Tam Kayıt
AlanDeğerDil
dc.contributor.authorAşiroǧlu, Batuhan
dc.contributor.authorMete, Büşta Rümeysa
dc.contributor.authorYildiz, Eyyüp
dc.contributor.authorNalçakan, Yaǧiz
dc.contributor.authorSezen, Alper
dc.contributor.authorDaǧtekin, Mustafa
dc.contributor.authorEnsari, Tolga
dc.date.accessioned2026-10-18T20:28:44.535972347Z
dc.date.available2026-10-18T20:28:44.535972347Z
dc.date.issued2019
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 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.
dc.identifier.doi10.1109/ebbt.2019.8741736
dc.identifier.isbn978-172811013-4
dc.identifier.scopus2-s2.0-85068579133
dc.identifier.startpage8741736
dc.identifier.urihttps://rdlopen.rawdatalibrary.net/r/ebyu/13940
dc.identifier.wosWOS:000491430200023
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectAutomatic code generation
dc.subjectConvolutional neural network
dc.subjectDeep learning
dc.subjectHTML
dc.subjectObject detection
dc.subjectObject recognition
dc.subjectBiomedical engineering
dc.subjectCodes (symbols)
dc.subjectDeep neural networks
dc.subjectElectronic medical equipment
dc.subjectMachine learning
dc.subjectMockups
dc.subjectNeural networks
dc.subjectWebsites
dc.subjectAutomatic code generations
dc.subjectCode Generation
dc.subjectComputer vision techniques
dc.subjectDesign cycle
dc.subjectGraphic design
dc.subjectLearning methods
dc.subjectMachine learning techniques
dc.subjectAutomatic programming
dc.titleAutomatic HTML code generation from mock-up images using machine learning techniques
dc.typeConference Object
dspace.entity.typePublication
rdl.citation.scopus42
rdl.citation.wos21
rdl.source.indexSCOPUS
rdl.source.indexWOS
rdl.wos.indexConference Proceedings Citation Index - Science (CPCI-S)