Summary of Khattat: Enhancing Readability and Concept Representation Of Semantic Typography, by Ahmed Hussein et al.
Khattat: Enhancing Readability and Concept Representation of Semantic Typography
by Ahmed Hussein, Alaa Elsetohy, Sama Hadhoud, Tameem Bakr, Yasser Rohaim, Badr AlKhamissi
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper introduces an end-to-end system for automating semantic typography, a complex task that involves selecting an idea, choosing an appropriate font, and balancing creativity with legibility. The proposed system uses a Large Language Model (LLM) to generate imagery ideas for words, particularly useful for abstract concepts like freedom. The FontCLIP pre-trained model then selects a suitable font based on its semantic understanding of font attributes. The system iteratively transforms characters using a pre-trained diffusion model and employs an OCR-based loss function to enhance readability. Compared to other baselines, the proposed method demonstrates great readability enhancement and versatility across multiple languages and writing scripts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a way to make text more expressive and meaningful while still being easy to read. The system uses a special computer program (LLM) to come up with ideas for what words could look like, based on their meaning. It then chooses the right font for each word, taking into account things like how big or small the letters should be. The system even helps make the text more readable by changing the shape of individual characters. This is a big deal because it means we can create text that is both artistic and easy to understand. |
Keywords
» Artificial intelligence » Diffusion model » Large language model » Loss function
