Speed Reading Tool Powered by Artificial Intelligence for Students with
  ADHD, Dyslexia, or Short Attention Span
Megat Irfan Zackry Bin Ismail Ahmad Nazran bin Yusri Muhammad Hafizzul Bin Abdul Manap Muhammad Muizzuddin Bin Kamarozaman
This paper presents a novel approach to assist students with dyslexia, ADHD,
and short attention span in digesting any text-based information more
efficiently. The proposed solution utilizes the Multilayer Perceptron (MLP)
algorithm for complex text processing and summarization tasks. The tool
leverages the T5 (Text-to-Text Transfer Transformer) model from Hugging Face,
which treats every NLP task as a text generation task. The model is fine-tuned
on specific tasks using a smaller dataset. The NLTK's Punkt Sentence Tokenizer
is used to divide a text into a list of sentences. The application is served
using Flask, a lightweight web server and framework. The tool also applies
principles from Bionic Reading to enhance readability, which includes a bolding
function and adjustments to line, word, and character spacing. The paper
discusses the methodology, implementation, and results of the AI-based speed
reading tool.