Scene Understanding in a Web Browser

This research study offers a novel strategy for improving scene interpretation in web browsers by combining text analysis, image processing, and sophisticated web scraping methods. First, we use the BeautifulSoup package for web scraping to gather textual and graphic data from websites. The Wikipedia API is then used to process this textual data, identifying and elaborating on important terms that improve the contextual comprehension of the information. VGG16 and long short-term memory (LSTM) networks are used to provide picture captioning, while YOLOv5 is used for object detection. We have combined these techniques to give a thorough scene analysis by recognizing, classifying, and describing the things in the pictures. Combining these methods enables a more complex and real-time comprehension of the scene displayed at a specific URL, including textual and visual components. This research makes a substantial contribution to the disciplines of web content analysis and automated scene understanding by demonstrating a hybrid multi-modal approach for the analysis of images and text.