Abstract:
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The rapid growth of visual content on the internet presents challenges and opportunities for accessibility and information retrieval. This project aims to enhance web accessibility by developing an intelligent browser extension that automates the process of generating descriptive captions for images on websites. The extension scrapes images from web pages, uploads them to a Flask server equipped with a pre-trained image captioning model, and replaces the alt text of each image with the generated captions.
The image captioning model is trained on a large dataset of images and corresponding captions, leveraging deep learning techniques to understand and describe visual content accurately. By integrating this model into a browser extension, the project seeks to improve the accessibility of web content for visually impaired users, enhance search engine optimization (SEO), and provide better content descriptions for various applications.
The implementation involves creating a seamless user experience where the extension runs in the background, identifying images, processing them, and updating the webpage without user intervention. The Flask server handles image processing and caption generation, ensuring scalability and efficiency. This project demonstrates the potential of combining web scraping, machine learning, and browser extensions to create tools that enhance the web's usability and accessibility.
Through this innovative solution, the project addresses the need for automated image descriptions, providing a practical application of artificial intelligence in improving web accessibility and enriching user experiences. The development and deployment processes, along with the challenges faced and solutions implemented, are documented to provide a comprehensive understanding of the project's scope and impact."
