Automatic HTML Generator from Sketch Using Deep Learning to Speed Up Web Developing Post-COVID-19

Yosi Kristian; Robert Subianto; Eka Rahayu Setyaningsih; Anik Nur Handayani

doi:10.1201/9781003331674-1

Automatic HTML Generator from Sketch Using Deep Learning to Speed Up Web Developing Post-COVID-19

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Yosi Kristian, Robert Subianto, Eka Rahayu Setyaningsih, Anik Nur Handayani

2024 The Spirit of Recovery: IT Perspectives, Experiences, and Applications during the COVID-19 Pandemic Book chapter Cited by 0

Abstract

The COVID-19 pandemic has accelerated the digitalization of economic activities. Accelerating online and mobile platform development is the best way to simplify digital buying and selling. Website development may be accelerated by automatically converting webpage sketch pictures to HTML. In this chapter, we explain how to create a website from a draft image automatically. We also introduce a deep learning system that can turn a website sketch image into a website that nearly resembles the sketch by comparing Xception, DeeplabV3+, and Unet. The DeeplabV3+ model with Xception backbone segmented sketch photos with a Jaccard score of 0.50 and recognized 18 of 20 HTML element classes. © 2024 selection and editorial matter, Aji Prasetya Wibawa.

Affiliations

Institut Sains dan Teknologi Terpadu Surabaya, Surabaya, Indonesia; Universitas Negeri Malang, Malang, Indonesia

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