Abstract
Text image is a unique and crucial information medium that integrates visual aesthetics and linguistic semantics in modern e-society. Due to their subtlety and complexity, the generation of text images represents a challenging and evolving frontier in the image generation field. The recent surge of specialized image generators (\emph{e.g.}, Flux-series) and unified generative models (\emph{e.g.}, GPT-4o), which demonstrate exceptional fidelity, raises a natural question: can they master the intricacies of text image generation and editing? Motivated by this, we assess current state-of-the-art generative models' capabilities in terms of text image generation and editing. We incorporate various typical optical character recognition (OCR) tasks into our evaluation and broaden the concept of text-based generation tasks into OCR generative tasks. We select 33 representative tasks and categorize them into five categories: document, handwritten text, scene text, artistic text, and complex \& layout-rich text. For comprehensive evaluation, we examine six models across both closed-source and open-source domains, using tailored, high-quality image inputs and prompts. Through this evaluation, we draw crucial observations and identify the weaknesses of current generative models for OCR tasks. We argue that photorealistic text image generation and editing should be internalized as foundational skills into general-domain generative models, rather than being delegated to specialized solutions, and we hope this empirical analysis can provide valuable insights for the community to achieve this goal. This evaluation is online and will be continuously updated at our GitHub repository.
Abstract (translated)
文本图像是一种在现代电子社会中集视觉美感与语言语义于一体的独特且关键的信息媒介。由于其微妙性和复杂性，生成文本图像是图像生成领域中的一个充满挑战和不断发展的前沿方向。近年来，专业化的图像生成器（如Flux系列）以及统一的生成模型（如GPT-4o），展现出非凡的真实感效果，这引发了一个自然的问题：它们能否掌握文本图像生成与编辑的复杂性？受到这一问题的启发，我们评估了当前最先进的生成模型在文本图像生成和编辑方面的能力。我们在评估中纳入了一系列典型的光学字符识别(OCR)任务，并将基于文本的生成任务的概念扩展到了OCR生成任务。我们选择了33个具有代表性的任务，并将其分类为五类：文档、手写文本、场景文本、艺术文本以及复杂且布局丰富的文本。为了进行全面评估，我们在闭源和开源领域中选取了六种模型，并使用定制的高质量图像输入与提示语进行测试。通过这一评价过程，我们得出了关键性的观察结果并识别出当前OCR任务中的生成模型存在的弱点。我们认为，逼真的文本图像生成和编辑应当被融入到通用领域的生成模型之中，而不是委派给专门的解决方案来处理，并希望这种实证分析能够为社区实现此目标提供宝贵的见解。此次评估是在线进行且将持续在我们的GitHub仓库中更新。 这段文字介绍了对当前最先进的生成模型在文本图像生成和编辑方面的能力进行了全面评估的研究工作。研究不仅涵盖了多种类型的OCR任务，还提出了将逼真的文本图像生成和编辑技术融入通用领域生成模型的重要性，并分享了持续更新的实证分析结果。
URL
https://arxiv.org/abs/2507.15085
