## 1 Introduction  
Text-to-image (T2I) generative models have emerged as a cornerstone of modern artificial intelligence, enabling the seamless synthesis of high-quality images from textual descriptions. These models have found applications in diverse domains, including creative content generation, educational tools, and assistive technologies. Despite their growing importance, evaluating the performance of T2I models remains a complex and evolving challenge. Standard evaluation metrics, such as Contrastive Language-Image Pre-training (CLIP) scores [1], Frechet Inception Distance (FID) [2], and Optical Character Recognition(OCR) accuracy [3], provide valuable information but often fall short in capturing the nuanced capabilities of these models, particularly for tasks involving intricate textual content. Moreover, traditional evaluation methodologies heavily rely on curated datasets that may not adequately represent real-world complexities or assess model robustness. Traditional OCR tools face significant limitations when evaluating text within images generated by T2I models, particularly when dealing with stylized fonts and complex layouts. These tools frequently produce unreliable results, including garbled text, omitted content, and misinterpreted symbols. For example, when analyzing the water cycle infographic shown in Figure 1, OCR systems struggled significantly, producing incomprehensible output that merged and distorted the text elements: \"THEWATERCYCLEWIVMMWWEVAPORATIONCONDENSATIONPRECIPITATION\". This demonstrates the fundamental limitations of current OCR-based evaluation methods. This study aims to address these fundamental challenges in T2I model evaluation by developing a comprehensive framework that can accurately assess text generation quality across various specialized contexts. To achieve this, our research introduces a novel evaluation framework that uses adaptive prompt engineering to comprehensively test T2I models. Rather than relying on static datasets, we employ GPT-4 to generate a diverse array of challenging prompts designed to rigorously evaluate model capabilities across multiple dimensions. These prompts encompass tasks such as rendering mathematical equations, multilingual text, and complex visual layouts, offering a robust assessment of model performance in real-world scenarios. In addition to generating prompts, GPT-4 serves as a human-like evaluator, providing nuanced, qualitative feedback that bridges the gap between traditional quantitative metrics and the perceptual insights typically offered by human evaluators. This dual role enables a richer and more comprehensive evaluation, particularly in areas where subjective judgment is crucial, such as readability, alignment, and contextual coherence of text. This research addresses the following key questions:  
1. How can we systematically evaluate the accuracy and reliability of text rendering in T2I-generated images?
2. What metrics and methodologies are most effective for assessing complex textual content in generated images?
3. How do current T2I models perform when generating specialized content such as mathematical equations, diagrams, and multi-line text?
4. What are the specific limitations and challenges in current T2I models regarding text rendering, and how can these be quantified?  
Our approach evaluates state-of-the-art T2I models in three critical dimensions: text-image alignment, layout complexity, and cross-lingual text rendering.  
Figure 1: OCR challenges for extracting text.  
The remainder of this paper is structured as follows. Section 2 reviews related work, highlighting existing evaluation frameworks and their limitations. Section 3 outlines our methodology, including the design of challenging prompts and evaluation criteria. Section 4 presents the experimental results that demonstrate the strengths and weaknesses of the models evaluated. Section 5 discusses the implications of our findings, and in Section 6 we conclude our work.