## 6 Conclusions  
In this paper, we present a comprehensive framework designed to evaluate and benchmark the performance of text-to-image (T2I) models in handling specialized and structured content. Alongside a detailed methodology, we have provided practical use cases to demonstrate how our framework can rigorously assess various aspects of T2I model outputs, including alignment, readability, and contextual accuracy. Through adaptive prompt engineering and GPT-4 evaluations, our framework closely approximates real-world conditions, enabling accurate assessments even with diverse and challenging textual scenarios. Our findings reveal significant limitations in T2I models, particularly in tasks requiring precision, such as generating chemical diagrams, programming code, and coherent multi-line text. Performance analysis shows that these models struggle with structural consistency and semantic alignment, with errors exceeding acceptable thresholds in many cases. These shortcomings highlight the critical need for improvements in areas such as text fidelity, formatting consistency, and cross-lingual adaptability. The systematic evaluation of T2I models presented in this study reveals important theoretical implications about current model architectures' fundamental challenges in maintaining semantic and structural consistency across complex textual content, particularly evident in their struggle with mathematical equations, chemical diagrams, and multi-line text generation. Our findings underscore the current limitations of T2I models in generating accurate specialized content, highlighting the need for significant improvements before these models can be reliably used for technical documentation, multilingual applications, or complex visual-textual tasks requiring precise formatting and semantic accuracy. Looking forward, research should focus on two critical directions: developing specialized fine-tuning approaches for precise text rendering capabilities in technical content (mathematical equations, chemical diagrams, and code snippets), and enhancing cross-lingual text generation capabilities through architectural innovations that better handle diverse writing systems while maintaining visual and semantic coherence. We summarize the key findings in Table 9. \begin{table}
\begin{tabular}{p{12.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Experiment** & **Category** & **Key Findings** & **Limitations \& Challenges** \\ \hline \multirow{8}{*}{1. Text-Image Alignment} & \multirow{4}{*}{Mathematical Content} & \(\bullet\) Visually plausible but structurally incorrect & \(\bullet\) Maintenance of mathematical symbol relationships \\  & & \(\bullet\) Poor symbol alignment & \(\bullet\) Proper equation formatting \\  & & \(\bullet\) Issues with operator placement & \(\bullet\) Complex symbolic computation \\ \cline{2-4}  & \multirow{4}{*}{Chemical Diagrams Alignment} & \(\bullet\) Struggles with bond angles and atom labeling & \(\bullet\) Precise structural alignment \\  & & \(\bullet\) Better visual design than accuracy & \(\bullet\) Complex molecular representations \\  & & \(\bullet\) Better visual design than accuracy & \(\bullet\) Specialized notation accuracy \\ \cline{2-4}  & \multirow{4}{*}{Programming Code} & \(\bullet\) Poor performance across all models & \(\bullet\) Maintaining code structure \\  & & \(\bullet\) Issues with indentation and syntax & \(\bullet\) Syntax highlighting \\ \cline{2-4}  & \multirow{4}{*}{Flowcharts} & \(\bullet\) Best overall performance category & \(\bullet\) Hierarchical relationships \\  & & \(\bullet\) Strong visual coherence & \(\bullet\) Logical flow maintenance \\ \cline{2-4}  & & \(\bullet\) Spatial organization \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Summary of text-to-image model experiments. **Author Contributions:** Conceptualization, Z.B. ; Methodology, Z.B. ; Data curation, Z.B. ; Writing--original draft, Z.B. ; Writing--review & editing, Z.B. and V.B. ; Supervision, V.B. All authors have read and agreed to the published version of the manuscript.