Year: 2025
Type: conference-paper
Abstract: We present a visual-language approach to Arabic readability assessment using the PIXEL Vision Transformer, which processes rendered text as images to bypass tokenization challenges.Our system participated in the BAREC 2025 Shared Task (Sentence-level Strict track).We evaluate orthographic variants (normalization, diacritization, transliteration) and morphological segmentation with different visual boundary markers.Results show that diacritization provides useful visual cues for disambiguation, morphological segmentation improves over word-level processing, and transliterated scripts outperform native Arabic script.Our approach demonstrates the potential of visual processing for readability assessment in complex languages and writing systems.
Author: Ben Sapirstein
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4416037818