Open AccessAutomated Web Design And Code Generation Using Deep Learning
Author(s) -
Vishaal Saravanan et.al
Publication year - 2021
Publication title -
türk bilgisayar ve matematik eğitimi dergisi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.218
H-Index - 3
ISSN - 1309-4653
DOI - 10.17762/turcomat.v12i6.1401
Subject(s) - computer science , softmax function , benchmark (surveying) , artificial intelligence , code (set theory) , pipeline (software) , convolutional neural network , snippet , code generation , workflow , deep learning , source code , scratch , web application , machine learning , pattern recognition (psychology) , programming language , operating system , database , geodesy , set (abstract data type) , key (lock) , geography
Excited by ground-breaking progress in automatic code generation, machine translation, and computer vision, further simplify web design workflow by making it easier and productive. A Model architecture is proposed for the generation of static web templates from hand-drawn images. The model pipeline uses the word-embedding technique succeeded by long short-term memory (LSTM) for code snippet prediction. Also, canny edge detection algorithm fitted with VGG19 convolutional neural net (CNN) and attention-based LSTM for web template generation. Extracted features are concatenated, and a terminal LSTM with a SoftMax function is called for final prediction. The proposed model is validated with a benchmark based on the BLUE score, and performance improvement is compared with the existing image generation algorithms.