Paper Contents
Abstract
An Integrated Development Environment (IDE) is a comprehensive tool that provides developers with essential features for software development, such as code editing, debugging, and execution. This project introduces an innovative IDE designed to efficiently convert hand-drawn sketches into functional web code. Transforming hand-drawn sketches into executable HTML code is challenging due to the variability in artistic styles and stroke patterns, along with the traditional separation between design and implementation. To address these issues, this project presents an IDE that utilizes advanced deep learning techniques to streamline the transition from sketches to code. The IDE incorporates the SketchNet Model, a Convolutional Neural Network (CNN) specifically designed to recognize and interpret web elements from hand-drawn sketches. To further enhance accuracy and efficiency, the SWIN Transformer is integrated, a transformer-based architecture known for its ability to capture long-range dependencies in images. This IDE provides a user-friendly platform where designers can sketch web elements directly. The SketchNet Model processes these sketches, and the SWIN Transformer ensures real-time detection accuracy. The system then generates the corresponding HTML code, which is executed within the IDE, allowing designers to instantly view their web interface. By bridging the gap between hand-drawn designs and functional web development, this IDE offers an intuitive and interactive environment, empowering designers with a direct link between their creative concepts and executable code, thus optimizing and streamlining the web development process.
Copyright
Copyright © 2025 INDUJA V.. This is an open access article distributed under the Creative Commons Attribution License.
