# Context-Aware Text-Based Binary Image Stylization and Synthesis

arXiv: https://arxiv.org/abs/1810.03767 · HTML: https://mlsanity.com/paper/1810.03767/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2018.

## Built on by (most cited first, 11 of 11)

- [FonTS: Text Rendering with Typography and Style Controls](https://mlsanity.com/paper/2412.00136/) (2024)
- [DS-Fusion: Artistic Typography via Discriminated and Stylized Diffusion](https://mlsanity.com/paper/2303.09604/) (2023)
- [De-rendering Stylized Texts](https://mlsanity.com/paper/2110.01890/) (2021)
- [WordCon: Word-level Typography Control in Scene Text Rendering](https://mlsanity.com/paper/2506.21276/) (2025)
- [Towards Diverse and Consistent Typography Generation](https://mlsanity.com/paper/2309.02099/) (2023)
- [FontStudio: Shape-Adaptive Diffusion Model for Coherent and Consistent Font Effect Generation](https://mlsanity.com/paper/2406.08392/) (2024)
- [Intelligent Artistic Typography: A Comprehensive Review of Artistic Text Design and Generation](https://mlsanity.com/paper/2407.14774/) (2024)
- [GenText: Unsupervised Artistic Text Generation via Decoupled Font and Texture Manipulation](https://mlsanity.com/paper/2207.09649/) (2022)
- [APRNet: Attention-based Pixel-wise Rendering Network for Photo-Realistic Text Image Generation](https://mlsanity.com/paper/2203.07705/) (2022)
- [Dynamic Texture Transfer using PatchMatch and Transformers](https://mlsanity.com/paper/2402.00606/) (2024)
- [FontCrafter: High-Fidelity Element-Driven Artistic Font Creation with Visual In-Context Generation](https://mlsanity.com/paper/2603.22054/) (2026)

## Abstract (arXiv)

In this work, we present a new framework for the stylization of text-based binary images. First, our method stylizes the stroke-based geometric shape like text, symbols and icons in the target binary image based on an input style image. Second, the composition of the stylized geometric shape and a background image is explored. To accomplish the task, we propose legibility-preserving structure and texture transfer algorithms, which progressively narrow the visual differences between the binary image and the style image. The stylization is then followed by a context-aware layout design algorithm, where cues for both seamlessness and aesthetics are employed to determine the optimal layout of the shape in the background. Given the layout, the binary image is seamlessly embedded into the background by texture synthesis under a context-aware boundary constraint. According to the contents of binary images, our method can be applied to many fields. We show that the proposed method is capable of addressing the unsupervised text stylization problem and is superior to state-of-the-art style transfer methods in automatic artistic typography creation. Besides, extensive experiments on various tasks, such as visual-textual presentation synthesis, icon/symbol rendering and structure-guided image inpainting, demonstrate the effectiveness of the proposed method.