Abstract:
"Within the field of web development, Web designing, Quality assurance testing and Usability
Testing providing exceptional user experiences is crucial. Still, it can be difficult to recognize
and resolve problems with UI design, front-end development, and quality assurance. This study
offers a comprehensive approach to address these issues by automating the analysis of web
components using screenshots and offering helpful details and suggestions for improving the
usability, accessibility, and general user experience. The technology effectively recognizes
web components and carries out thorough evaluations to find possible areas for development
using cutting-edge deep learning techniques.
By using an inventive method, the system is quite effective at recognizing web elements from
PNG screenshots and offers insightful information about the online environment. It is important
to note that it uses deep learning methods to improve its ability to recognize objects like
buttons, photos, URLs, iframes, and more. The interface is thoroughly understood by the
system through the composition and layout analysis of web pages, which helps it recognize
possible issues with accessibility and usability.
The technology helps web designers, and web developers to improve their digital products and
make sure they closely match consumer expectations by providing them with practical advice
based on data analysis. Developers can prioritize enhancements that lead to significant
engagement and satisfaction by focusing on the fundamental components of web design and
user experience. In the end, this strategy supports a culture of continuous improvement where
small enhancements to web applications are guided by user input and data-driven insights."
