TextAtlas5M: A Large-scale Dataset for Dense Text Image Generation
Authors: name: Alex Jinpeng Wang; name: Dongxing Mao; name: Jiawei Zhang; name: Weiming Han; name: Zhuobai Dong; name: Linjie Li; name: Yiqi Lin; name: Zhengyuan Yang; name: Libo Qin; name: Fuwei Zhang; name: Lijuan Wang; name: Min Li
Published: 2025-02-11 18:59:19

Abstract: Text-conditioned image generation has gained significant attention in recent
years and are processing increasingly longer and comprehensive text prompt. In
everyday life, dense and intricate text appears in contexts like
advertisements, infographics, and signage, where the integration of both text
and visuals is essential for conveying complex information. However, despite
these advances, the generation of images containing long-form text remains a
persistent challenge, largely due to the limitations of existing datasets,
which often focus on shorter and simpler text. To address this gap, we
introduce TextAtlas5M, a novel dataset specifically designed to evaluate
long-text rendering in text-conditioned image generation. Our dataset consists
of 5 million long-text generated and collected images across diverse data
types, enabling comprehensive evaluation of large-scale generative models on
long-text image generation. We further curate 3000 human-improved test set
TextAtlasEval across 3 data domains, establishing one of the most extensive
benchmarks for text-conditioned generation. Evaluations suggest that the
TextAtlasEval benchmarks present significant challenges even for the most
advanced proprietary models (e.g. GPT4o with DallE-3), while their open-source
counterparts show an even larger performance gap. These evidences position
TextAtlas5M as a valuable dataset for training and evaluating future-generation
text-conditioned image generation models.