Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{152018,
        author = {Payal and Sushma JC and Thanmaya C and Sushma D Sarang and Mrs. Vidhya K},
        title = {Automatic HTML Code Generation from Mock-up Images Using Machine Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {2},
        pages = {204-208},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=152018},
        abstract = {The design cycle for a website begins with the construction of individual web page mock-ups, which can be done by hand or with the help of graphic design and specialist mock-up production tools. Software programmers next turn the prototype into structured HTML or comparable markup code. This procedure is typically performed several times until the appropriate template is obtained. The goal of this research is to automate the process of creating code from hand-drawn mock-ups. Computer vision techniques are utilized to process hand-drawn mock-ups, and then deep learning approaches are employed to construct the suggested system. Our system has a method accuracy of 96 percent and a validation accuracy of 73 percent.},
        keywords = {Object detection, object recognition, convolutional neural network, deep learning, automatic code generation, HTML},
        month = {},
        }