Published in: Journal of Computer Science and Engineering in Innovations and Research (ISSN No: 3049-1762 online)
Publication Date: May 15, 2025
This study presents an innovative AI-based framework for the automated analysis of website content and the optimization of design, using computer vision and large language models (LLMs). The proposed system comprises a three-phase pipeline: structural extraction, comparative benchmarking, and intelligent content development.
The system first gathers full-page screenshots of a target website and then separates them into functional components, including headers, sliders, and content blocks, using computer vision algorithms. These components are compared to a curated library of design features to evaluate structural patterns.
In the second step, the system conducts comparative analyses across various websites to extract benchmarking insights and establish standard design metrics. Ultimately, the framework merges these insights to provide alternative design ideas, augmented with contextually relevant textual and visual content generated by LLMs.
This cohesive strategy expedites the web development process while enhancing visual consistency and user experience. The method shows potential in combining visual structural understanding with generative AI to produce intelligent, data-driven website optimization and content automation.
Computer Vision, Website Analysis, Image Segmentation, Content Generation, Large Language Models, Design Optimization, Content Matching, Automated Recommendations