AI Font Generator: an AI project by Christian Boris Niederreuther and Johannes Schall
Science & Technology
AI Font Generator: An AI Project by Christian Boris Niederreuther and Johannes Schall
Introduction
Thank you very much, Jose, for the introduction. I was working on a project with a colleague from Munich who, unfortunately, is stuck there in snowy conditions. Despite our plans, we have resorted to presenting via video. Here is a recorded introduction from him for his part.
Video Introduction
"Hello, first of all, let me express my regret for presenting to you via recording today. I had hoped to be in Berlin and present our project in person. However, weather conditions and transfer issues here in Munich made that impossible. This recording is the only way to present today. Without further ado, let me introduce our project—the AI Font Generator.
The Problem and Market
When discussing writing systems, you're looking at thousands of years of history. Here, you can see significant historical milestones. Given this long history, one might think everything about fonts, letters, or glyphs has already been done. However, there's still a billion-dollar market out there. Fonts are typically designed manually, an exhaustive task that often leads designers to take shortcuts, such as omitting less commonly used glyphs. For German designers, this often means a lack of necessary characters like the German umlaut, creating problems in usability. Our AI Font Generator tackles this issue by generating missing glyphs in the same style as the original font set.
Data Collection & Cleaning
We downloaded about 50,000 fonts from publicly available sources like Google Fonts and other databases. Initially, we applied filters to identify and remove corrupted files and fonts with missing glyphs. Amazingly, about 50% of the data we downloaded was filtered out, with many fonts containing random or garbage characters instead of the proper glyphs. To address this, we used a classifier in combination with OpenAI's CLIP to identify and remove these incorrect glyphs.
Model Architecture
After rigorous data cleaning, we structured our model to ensure it met our primary objectives: generating the missing characters, especially for German fonts, and creating a model extendable for future development. We opted for an encoder-decoder structure to retain the font's style and generate new glyphs accordingly.
We experimented with two approaches: one model considered multiple characters at once, while the other focused on individual character generation. The results showed that the first model, trained from scratch with multiple characters, performed better.
Results and Implementation
Both models produced promising results, with the first model performing better due to its design. The generated characters maintained the font style accurately. We uploaded our project on Hugging Face, where users can upload font files with missing German characters and generate the necessary glyphs.
Future Work
Our next steps include converting the generated graphics into vector graphics, automatically integrating them back into the font files, and training the model on the complete dataset. We also plan to incorporate an NLP encoder for style translation, enabling font generation from a simple text prompt.
Acknowledgments
Thanks to Boris for the collaboration, our mentor Imram, and everyone involved in the project.
Keywords
- AI Font Generator
- Glyphs
- Encoder-Decoder Model
- Font Market
- Data Cleaning
- CLIP by OpenAI
- Model Architecture
- Hugging Face * Font Generation
FAQ
What is the AI Font Generator?
The AI Font Generator is a project developed to generate missing glyphs in font sets, particularly catering to German characters like the umlaut.
Why is this project important?
Many fonts available online do not include all necessary characters, especially specialized characters for specific languages. This project aims to fill that gap, significantly benefiting designers.
How does the model work?
The model uses an encoder-decoder structure. The encoder interprets existing characters to understand the font's style, and the decoder generates the missing characters in the same style.
What was the role of CLIP by OpenAI?
CLIP was used to classify and filter out incorrect or garbage glyphs from our dataset, ensuring that only the appropriate characters were used for model training.
What are the future goals of the project?
Future goals include converting the generated glyphs into vector graphics, integrating them back into font files, training on an expanded dataset, and implementing an NLP encoder for generating fonts from text descriptions.