Enhancing Tamil Text Readability for Dyslexic Readers Using Generative AI Based Text Simplification

Abstract

ABSTRACT Dyslexia is a commonly recognized reading disorder impacting reading fluency, comprehension and cognitive processing. While extensive research has been conducted on assistive technologies for dyslexic readers, most solutions are primarily designed for high resource languages such as English. Consequently, accessibility tools for Tamil readers remain scarce. The agglutinative morphological complexity of the Tamil language, along with its extensive use of ligatures and visually similar characters, further exacerbates reading difficulties for dyslexic individuals. This study addresses this research gap by proposing a Large Language Model based text simplification approach aimed at improving text readability while maintaining semantic coherence. Adopting a Design Science Research methodology, this study integrates few-shot learning, prompt engineering, and rule-based text simplification techniques to enhance Tamil text accessibility. A custom curated dataset incorporating Tamil text with predefined simplification rules was developed and used to evaluate the performance of various LLMs, including BART, T5 variants and LLaMA variants. Experimental evaluations were conducted using both qualitative feedback and quantitative metrics to assess the effectiveness of these models. The findings indicate that prompt engineering alone resulted in limited improvements, whereas fine-tuning domain-specific LLMs trained on Tamil linguistic rules demonstrated greater efficacy in generating simplified text. However, challenges persist, particularly in adapting LLMs to Tamil’s intricate linguistic structure and ensuring that readability enhancements align with dyslexia-friendly formatting standards. This research contributes to the broader field of Natural Language Processing for low-resource languages by demonstrating the feasibility of LLM driven Tamil text simplification. Future research directions may include fine-tuning larger Tamil-specific LLMs, integrating reinforcement learning with human feedback, and deploying real-time text simplification models for assistive applications. By addressing the accessibility needs of Tamil-speaking dyslexic readers, this study paves the way for inclusive AI-driven text simplification solutions, ultimately improving the readability and comprehensibility of complex textual content.