TextGround4M: A Prompt-Aligned Dataset for Layout-Aware Text Rendering

• URL: http://arxiv.org/abs/2604.24459v1
• Date: Mon, 27 Apr 2026 13:28:57 GMT
• Title: TextGround4M: A Prompt-Aligned Dataset for Layout-Aware Text Rendering
• Abstract summary: Despite advances in text-to-image generation, models still struggle to render prompt-specified text with correct spatial layout.<n>We introduce TextGround4M, a dataset of over 4 million prompt-image pairs annotated with span-level text grounded in the prompt and corresponding bounding boxes.<n>We propose a lightweight training strategy for autoregressive T2I models that appends layout-aware span tokens during training, without altering model architecture or inference behavior.
• Score: 64.22226877213521
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite recent advances in text-to-image generation, models still struggle to accurately render prompt-specified text with correct spatial layout -- especially in multi-span, structured settings. This challenge is driven not only by the lack of datasets that align prompts with the exact text and layout expected in the image, but also by the absence of effective metrics for evaluating layout quality. To address these issues, we introduce TextGround4M, a large-scale dataset of over 4 million prompt-image pairs, each annotated with span-level text grounded in the prompt and corresponding bounding boxes. This enables fine-grained supervision for layout-aware, prompt-grounded text rendering. Building on this, we propose a lightweight training strategy for autoregressive T2I models that appends layout-aware span tokens during training, without altering model architecture or inference behavior. We further construct a benchmark with stratified layout complexity to evaluate both open-source and proprietary models in a zero-shot setting. In addition, we introduce two layout-aware metrics to address the long-standing lack of spatial evaluation in text rendering. Our results show that models trained on TextGround4M outperform strong baselines in text fidelity, spatial accuracy, and prompt consistency, highlighting the importance of fine-grained layout supervision for grounded T2I generation.

Related papers

• TextSculptor: Training and Benchmarking Scene Text Editing [88.11688559021628]
  We present TextSculptor, a comprehensive framework for data construction and evaluation of scene text editing.<n>TextSculptor improves open-source text editing performance and narrows the gap to proprietary models.
  arXiv  Detail & Related papers  (2026-05-20T12:22:26Z)
• TextGuider: Training-Free Guidance for Text Rendering via Attention Alignment [68.91073792449201]
  We propose TextGuider, a training-free method that encourages accurate and complete text appearance.<n>Specifically, we analyze attention patterns in Multi-Modal Diffusion Transformer(MM-DiT) models, particularly for text-related tokens intended to be rendered in the image.<n>Our method achieves state-of-the-art performance in test-time text rendering, with significant gains in recall and strong results in OCR accuracy and CLIP score.
  arXiv  Detail & Related papers  (2025-12-10T06:18:30Z)
• UniGlyph: Unified Segmentation-Conditioned Diffusion for Precise Visual Text Synthesis [38.658170067715965]
  We propose a segmentation-guided framework that uses pixel-level visual text masks as unified conditional inputs.<n>Our approach achieves state-of-the-art performance on the AnyText benchmark.<n>We also introduce two new benchmarks: GlyphMM-benchmark for testing layout and glyph consistency in complex, and MiniText-benchmark for assessing generation quality in small-scale text regions.
  arXiv  Detail & Related papers  (2025-07-01T17:42:19Z)
• Beyond Flat Text: Dual Self-inherited Guidance for Visual Text Generation [17.552733309504486]
  In real-world images, slanted or curved texts, especially those on cans, banners, or badges, appear as frequently as flat texts due to artistic design or layout constraints.<n>We introduce a new training-free framework, STGen, which accurately generates visual texts in challenging scenarios.
  arXiv  Detail & Related papers  (2025-01-10T11:44:59Z)
• DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization [15.920735314050296]
  This study decomposes the text embedding matrix and conducts a component analysis to understand the embedding space geometry.<n>We propose DECOR, which projects text embeddings onto a vector space to undesired token vectors.<n> Experimental results demonstrate that DECOR outperforms state-of-the-art customization models.
  arXiv  Detail & Related papers  (2024-12-12T10:59:44Z)
• Object-Attribute Binding in Text-to-Image Generation: Evaluation and Control [58.37323932401379]
  Current diffusion models create images given a text prompt as input but struggle to correctly bind attributes mentioned in the text to the right objects in the image. We propose focused cross-attention (FCA) that controls the visual attention maps by syntactic constraints found in the input sentence. We show substantial improvements in T2I generation and especially its attribute-object binding on several datasets.
  arXiv  Detail & Related papers  (2024-04-21T20:26:46Z)
• A Parse-Then-Place Approach for Generating Graphic Layouts from Textual Descriptions [50.469491454128246]
  We use text as the guidance to create graphic layouts, i.e., Text-to-labeled, aiming to lower the design barriers. Text-to-labeled is a challenging task, because it needs to consider the implicit, combined, and incomplete constraints from text. We present a two-stage approach, named parse-then-place, to address this problem.
  arXiv  Detail & Related papers  (2023-08-24T10:37:00Z)
• TextDiffuser: Diffusion Models as Text Painters [118.30923824681642]
  We introduce TextDiffuser, focusing on generating images with visually appealing text that is coherent with backgrounds. We contribute the first large-scale text images dataset with OCR annotations, MARIO-10M, containing 10 million image-text pairs. We show that TextDiffuser is flexible and controllable to create high-quality text images using text prompts alone or together with text template images, and conduct text inpainting to reconstruct incomplete images with text.
  arXiv  Detail & Related papers  (2023-05-18T10:16:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.