News2Braille: A T5-based News Summarization System for the Visually Impaired

Access to real-time news has become increasingly seamless in the digital age; however, visually impaired individuals often face challenges in navigating conventional news platforms. This research presents a novel T5 Transformer-Based News Summarizer and Braille Translator, designed to enhance news accessibility for the visually impaired. The proposed system comprises three core components: extraction of relevant textual content from online articles using the Newspaper3k library; abstractive summarization using a Fine-tuned Text-to-Text Transfer Transformer (T5) model to retain essential information; and conversion of the summarized content into Braille, enabling tactile reading.This integrated approach empowers users to access concise news content independently, without reliance on multiple tools or applications. Future enhancements may include implementing deep learning-based Braille translation, incorporating Optical Character Recognition (OCR) for printed news, and integrating text-to-speech functionality for auditory consumption. Overall, this work contributes toward a more inclusive and accessible digital ecosystem for visually impaired individuals.