Automated HTML Code Generation from Hand Drawn Images using Machine Learning Methods. (KEY IJP************092)

• Ms. P Swathi,Mr. Prajwal V,Mr. Abhishek B M,Prof. Deepa S Bhat

Abstract

This paper introduces a novel method for deep learning-based HTML code generation from hand-drawn pictures. The suggested technique uses a convolutional neural network (CNN) to figure out how to transfer hand-drawn diagrams to the corresponding HTML code. The CNN is trained using a sizable dataset of HTML code and annotated hand-drawn illustrations. The trained model is fed a hand-drawn drawing during testing, and the model outputs the equivalent HTML code. The results show that the suggested method is effective at producing precise HTML code when applied to a series of hand-drawn designs. The suggested method could greatly cut down on the time and work needed for web development, especially for designers that like to sketch their concepts instead of using computer-aided design tools.