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
Automatic 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 programming
RDL Kalıcı Bağlantı (PID)
https://rdlopen.rawdatalibrary.net/r/ebyu/13940Resmi atıflarda kullanılan kalıcı adresTam Kayıt
| Alan | Değer | Dil |
|---|---|---|
| dc.contributor.author | Aşiroǧlu, Batuhan | |
| dc.contributor.author | Mete, Büşta Rümeysa | |
| dc.contributor.author | Yildiz, Eyyüp | |
| dc.contributor.author | Nalçakan, Yaǧiz | |
| dc.contributor.author | Sezen, Alper | |
| dc.contributor.author | Daǧtekin, Mustafa | |
| dc.contributor.author | Ensari, Tolga | |
| dc.date.accessioned | 2026-10-18T20:28:44.535972347Z | |
| dc.date.available | 2026-10-18T20:28:44.535972347Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | 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. | |
| dc.identifier.doi | 10.1109/ebbt.2019.8741736 | |
| dc.identifier.isbn | 978-172811013-4 | |
| dc.identifier.scopus | 2-s2.0-85068579133 | |
| dc.identifier.startpage | 8741736 | |
| dc.identifier.uri | https://rdlopen.rawdatalibrary.net/r/ebyu/13940 | |
| dc.identifier.wos | WOS:000491430200023 | |
| dc.language.iso | en | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.relation.ispartof | 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019 | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | Automatic code generation | |
| dc.subject | Convolutional neural network | |
| dc.subject | Deep learning | |
| dc.subject | HTML | |
| dc.subject | Object detection | |
| dc.subject | Object recognition | |
| dc.subject | Biomedical engineering | |
| dc.subject | Codes (symbols) | |
| dc.subject | Deep neural networks | |
| dc.subject | Electronic medical equipment | |
| dc.subject | Machine learning | |
| dc.subject | Mockups | |
| dc.subject | Neural networks | |
| dc.subject | Websites | |
| dc.subject | Automatic code generations | |
| dc.subject | Code Generation | |
| dc.subject | Computer vision techniques | |
| dc.subject | Design cycle | |
| dc.subject | Graphic design | |
| dc.subject | Learning methods | |
| dc.subject | Machine learning techniques | |
| dc.subject | Automatic programming | |
| dc.title | Automatic HTML code generation from mock-up images using machine learning techniques | |
| dc.type | Conference Object | |
| dspace.entity.type | Publication | |
| rdl.citation.scopus | 42 | |
| rdl.citation.wos | 21 | |
| rdl.source.index | SCOPUS | |
| rdl.source.index | WOS | |
| rdl.wos.index | Conference Proceedings Citation Index - Science (CPCI-S) |
