HTML Code Generation from Website Images and Sketches using Deep Learning-Based Encoder-Decoder Model
D Yashaswini, Sneha Sneha, Nikhil Kumar · 2022
Making mockups of the website’s numerous pages is the first step in website design. Mock-ups can be created manually, using graphic design software, or using specialist tools. Then, software engineers turn the mock-up into structured HTML code. It takes a lot of time and effort to build the required template by repeating this method several times. This work proposes two deep learning-based encoder-decoder models that automatically generate HTML (Hypertext Markup Language) code from screenshot images of web pages and hand-drawn sketches. The webpage images are pre-processed using grayscaling, binarization and contour detection techniques before being handed over to the model as input. With an F1score of 0.91, the CNN-LSTM-based encoder-decoder model has outclassed the RNN-LSTM-based encoder-decoder model, which has an F1-score of 0.74.