title: TextAtlas5M: A Large-scale Dataset for Dense Text Image Generation
summary: 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.
paper_authors._id: 67ad79cb60ec3f444b21cbcb
paper_authors.name: Alex Jinpeng Wang
paper_authors._id: 67ad79cb60ec3f444b21cbcc
paper_authors.name: Dongxing Mao
paper_authors._id: 67ad79cb60ec3f444b21cbcd
paper_authors.name: Jiawei Zhang
paper_authors._id: 67ad79cb60ec3f444b21cbce
paper_authors.name: Weiming Han
paper_authors._id: 67ad79cb60ec3f444b21cbcf
paper_authors.name: Zhuobai Dong
paper_authors._id: 67ad79cb60ec3f444b21cbd0
paper_authors.name: Linjie Li
paper_authors._id: 67ad79cb60ec3f444b21cbd1
paper_authors.name: Yiqi Lin
paper_authors._id: 67ad79cb60ec3f444b21cbd2
paper_authors.name: Zhengyuan Yang
paper_authors._id: 67ad79cb60ec3f444b21cbd3
paper_authors.name: Libo Qin
paper_authors._id: 67ad79cb60ec3f444b21cbd4
paper_authors.name: Fuwei Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-13T20:35:54.519Z
paper_authors._id: 615181cd0d6899cb526dbd8d
paper_authors.avatarUrl: /avatars/4e29a953db4edee0abfc3485be4faf3d.svg
paper_authors.fullname: zhangfuwei
paper_authors.type: user
paper_authors.user: fuwei
paper_authors._id: 67ad79cb60ec3f444b21cbd5
paper_authors.name: Lijuan Wang
paper_authors._id: 67ad79cb60ec3f444b21cbd6
paper_authors.name: Min Li
paper_title: TextAtlas5M: A Large-scale Dataset for Dense Text Image Generation
paper_summary: 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.
paper_ai_keywords: Text-conditioned image generation TextAtlas5M long-text rendering large-scale generative models TextAtlasEval FlashAttention FlashAttention with DallE-3 open-source counterparts