# Automatic Text Box Placement for Supporting Typographic Design

arXiv: https://arxiv.org/abs/2510.07665 · HTML: https://mlsanity.com/paper/2510.07665/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 19 references that are in the map.
- First posted on arXiv 9 Oct 2025.

## Builds on (most cited first, 15 of 15)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://mlsanity.com/paper/1911.08287/) (2019)
- [LayoutGPT: Compositional Visual Planning and Generation with Large Language Models](https://mlsanity.com/paper/2305.15393/) (2023)
- [LayoutDM: Discrete Diffusion Model for Controllable Layout Generation](https://mlsanity.com/paper/2303.08137/) (2023)
- [LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators](https://mlsanity.com/paper/1901.06767/) (2019)
- [LayoutVAE: Stochastic Scene Layout Generation From a Label Set](https://mlsanity.com/paper/1907.10719/) (2019)
- [CanvasVAE: Learning to Generate Vector Graphic Documents](https://mlsanity.com/paper/2108.01249/) (2021)
- [LayoutPrompter: Awaken the Design Ability of Large Language Models](https://mlsanity.com/paper/2311.06495/) (2023)
- [PosterLLaVa: Constructing a Unified Multi-modal Layout Generator with LLM](https://mlsanity.com/paper/2406.02884/) (2024)
- [Relation-Aware Diffusion Model for Controllable Poster Layout Generation](https://mlsanity.com/paper/2306.09086/) (2023)
- [Retrieval-Augmented Layout Transformer for Content-Aware Layout Generation](https://mlsanity.com/paper/2311.13602/) (2023)
- [PosterLlama: Bridging Design Ability of Langauge Model to Contents-Aware Layout Generation](https://mlsanity.com/paper/2404.00995/) (2024)
- [Towards Flexible Multi-modal Document Models](https://mlsanity.com/paper/2303.18248/) (2023)

## Abstract (arXiv)

In layout design for advertisements and web pages, balancing visual appeal and communication efficiency is crucial. This study examines automated text box placement in incomplete layouts, comparing a standard Transformer-based method, a small Vision and Language Model (Phi3.5-vision), a large pretrained VLM (Gemini), and an extended Transformer that processes multiple images. Evaluations on the Crello dataset show the standard Transformer-based models generally outperform VLM-based approaches, particularly when incorporating richer appearance information. However, all methods face challenges with very small text or densely populated layouts. These findings highlight the benefits of task-specific architectures and suggest avenues for further improvement in automated layout design.