Template-Based Text-to-Image Alignment for Language Accessibility A Study on Visualizing Text Simplifications (2025)

Abstract: Individuals with intellectual disabilities often have difficulties in comprehending complex texts. While many text-to-image models prioritize photorealism over cognitive accessibility it is not clear how visual illustrations relate to text simplifications TS generated from them. This paper presents a structured vision language model VLM prompting framework for generating cognitively accessible images from simplified texts. We designed five prompt templates i.e. Basic Object Focus Contextual Scene Educational Layout Multi-Level Detail and Grid Layout each following distinct spatial arrangements while adhering to accessibility constraints such as object count limits spatial separation and content restrictions. Using 400 sentence-level TS pairs from four established text simplification datasets OneStopEnglish SimPA Wikipedia ASSET we conducted a two-phase evaluation Phase 1 assessed template effectiveness with CLIP similarity scores and Phase 2 involved expert annotation of generated images across ten visual styles by four accessibility specialists. Results show that the Basic Object Focus template achieved the highest semantic alignment indicating that visual minimalism enhances accessibility. Expert evaluation further identified Retro style as the most accessible and Wikipedia as the most effective text source. Inter-annotator agreement varied across dimensions with Text Simplicity showing strong reliability and Image Quality proving more subjective. Overall our framework offers practical guidelines for accessible content creation and underscores the importance of structured prompting in AI-generated visual accessibility tools.

## introduction individuals with intellectual disabilities often have difficulties understanding complex texts @xcite . while text simplification (ts) improves readability, it is frequently insufficient on its own. research shows that visual support, recommended by easy-to-read guidelines, can significantly enhance comprehension @xcite . however, most textto-image models prioritize aesthetics over accessibility, which can cause cluttered, abstract, or semantically misaligned visuals generated from text inputs. recent studies have explored image retrieval for simplified text as a potential solution @xcite . however, none have systematically investigated structured prompting for accessible image generation in the ts context. in this work, we address this gap by introducing a template-based framework that explicitly enforces accessibility constraints, such as controlled object count, spatial separation, and the absence of embedded text, directly within prompts for querying vision-language models (vlms). using 400 text simplification pairs from four datasets (asset, onestopenglish, simpa, and wikipedia), we generated and evaluated 4,000 images across five prompt templates and ten visual styles. our study aims to answer the following research questions (rqs): • rq1: how can template-based prompting improve the accessibility of images generated from simplified texts? • rq2: which visual styles and data sources are most effective for accessible image generation? • rq3: how do expert annotations compare to automatic evaluation metrics in assessing accessibility? our results show that the basic object focus template performs best, supporting visual minimalism. experts rate retro style and wikipediasourced simplifications as most accessible. we also find weak correlation between clipscores and human judgments, underscoring the need for human-centered evaluation in accessible and inclusive ai. ## methodology our study follows a two-phase pipeline. in phase 1, we developed and evaluated five prompt templates used by vlms for generating visuals, each implementing unique accessibility constraints while sharing core principles such as: • controlled object counts: defining the quantity of visual components present in the generated images; • spatial separation: checking if visual components are physically well-arranged; • exclusion of text or abstract elements: examining whether vlms render texts in generated images, which can cause additional confusion. in phase 2, we take the best-performing template and apply it to produce a large-scale multimodal dataset. we generated 4,000 images from 400 simplified sentences, spanning ten visual styles, with style-specific prompt adjustments (see appendix c.2 for an illustration of how a single sentence generates multiple style-specific prompts). four expert annotators then assessed the resulting text-image pairs across six dimensions using predefined criteria designed to capture both quantitative and qualitative accessibility aspects. while we acknowledge that images are linked to textual units of varying granularity, operating on the sentence level throughout was a means of isolating the effect of text unit size. ## results a total of 976 annotations were collected from four experts (a, k, l, m), corresponding to 37.5% of the initially planned 2,600 ( ## conclusion this work investigated template-based prompting for generating cognitively accessible images from simplified text. the basic object focus template proved most effective, showing that minimalism and object isolation enhance clarity. wikipedia and asset emerged as the most suitable datasets, while retro and realistic visuals supported accessibility better than abstract styles. expert evaluations highlighted systematic disagreement on subjective dimensions, and clipscores showed only weak alignment with human judgments. overall, our findings demonstrate that structured prompting improves accessibility but current vlms remain limited, underscoring the need for better models, clearer annotation protocols, and continued integration of human expertise.