## 3 Materials and Methods
### Categories of Prompts  
The evaluation framework includes three experiments designed to rigorously assess the ability of text-to-image generative models to handle diverse and challenging textual prompts. Experiment 1 focuses on evaluating the alignment of textual prompts with corresponding visual elements across specialized domains such as mathematics, chemistry, programming, and diagrams. Prompts include mathematical equations on chalkboards, chemical notations like periodic tables and molecular structures, programming code snippets, and algorithmic flowcharts, testing the models' precision in semantic accuracy, contextual coherence, and domain-specific formatting. Experiment 2 explores the models' capacity to handle complex layout structures, assessing their ability to generate coherent outputs across various text arrangements, including multi-line formats, dynamically sized or positioned text, curved layouts, and intricate visual integrations such as posters, scientific papers, and legal documents. This experiment provides insights into how well models adapt to real-world scenarios that require precision in layout and design complexity. Finally, Experiment 3 evaluates the models' cross-lingual text rendering capabilities, testing their performance in generating accurate, legible, and stylistically consistent text across a range of linguistic systems. These include alphabetic (e.g., English, Russian), logographic (e.g., Chinese, Japanese), abjad (e.g., Arabic), abugida (e.g., Hindi, Bengali), and syllabic scripts (e.g., Korean Hangul). The prompts cover simple single-line phrases, multi-line text structures, and creative combinations of mixed languages, challenging the models to maintain clarity, coherence, and visual harmony. Together, these experiments offer a comprehensive assessment of semantic precision, layout adaptability, and linguistic versatility, highlighting the strengths and limitations of state-of-the-art text-to-image models like DALL-E 3, Ideogram, and Stable Diffusion 3.5 Large. Based on these experimental requirements, we developed three primary categories of prompts to systematically evaluate model performance:  
* Text-image alignment and contextual coherence: Prompts in this category test the model's ability to render text in alignment with corresponding visual elements. Examples include mathematical equations, chemical diagrams, code, and flowcharts that require accurate placement and contextual integration.
* Complex layouts: This category evaluates the model's handling of intricate text structures, such as curved text, multi-line arrangements, and text embedded in complex visual designs.
* Multilingual text: To assess adaptability, the prompts incorporate scripts from various languages, challenging models to generate text in diverse writing systems while maintaining semantic accuracy and visual clarity.  
To ensure comprehensive testing, our framework generates adaptive prompts tailored to specific domains and tasks. These prompts are designed to challenge models with varying degrees of complexity, such as the need for precise formatting, semantic alignment, and compositional reasoning. By encompassing a broad spectrum of textual and visual challenges, the methodology captures the nuanced performance of models in multiple scenarios.