CVTG-2K is a benchmark for Complex Visual Text Generation (CVTG) containing 2,000 prompts designed to evaluate text rendering in generated images. According to the dataset card on Hugging Face and the TextCrafter (arXiv:2503.23461) paper that introduces it, prompts were generated via OpenAI's O1-mini API (using chain-of-thought techniques) and cover diverse scenes such as street views, advertisements, and book covers. The dataset emphasizes longer visual texts (mean ~8.10 words / ~39.47 characters) and multiple text regions (2–5 regions per prompt). About half the prompts include stylistic attributes (size, color, font). CVTG-2K provides fine-grained, decoupled prompt structures and carrier words to express text–position relationships, making it suitable for evaluating multi-region text rendering and stylization in text-conditioned image generation. Evaluation metrics reported for CVTG tasks include Word Accuracy and Normalized Edit Distance (NED). (Sources: Hugging Face dataset card for dnkdnk/CVTG-2K and TextCrafter paper, arXiv:2503.23461.)
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