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@article{191512,
        author = {V. Girija and C. Senthil Kumaran},
        title = {AUTOMATIC HTML CODE GENERATION FROM HAND DRAWN IMAGES USING MACHINE LEARNING TECHNIQUES},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {6886-6889},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191512},
        abstract = {The increasing need for rapid web interface development has encouraged automation of the design-to-code process using artificial intelligence. Traditionally, converting hand-drawn or digital mock-up into HTML/CSS requires significant manual labour and time. AutoCodeVision addresses this gap by using deep learning and computer vision techniques to convert UI mock-up images directly into structured HTML code, ensuring accuracy and consistency. The system integrates a Flask backend, SQLite database, and a CNN–OpenCV-based processing engine, enabling reliable component detection and code generation using established AI frameworks such as TensorFlow and Pitch. With role-based access, secure validation, and deployment compatibility, AutoCodeVision offers a complete, automated, and scalable web development workflow.},
        keywords = {Artificial Intelligence, Deep Learning, Computer Vision, Convolutional Neural Networks, OpenCV, Mock-up-to-Code Conversion, HTML/CSS Generation, Flask Framework, Web Automation, UI Layout Recognition.},
        month = {January},
        }