AI Based Font Pair Suggestion Modelling For Graphic Design
This addresses a domain-specific problem for graphic design tools by providing an automated, scalable solution for font pairing, though it appears incremental as it builds on existing embedding and language model techniques.
The paper tackles the challenge of selecting contextually relevant and novel fonts for AI-generated designs in Microsoft Designer, developing a system that uses font visual embeddings, stroke width algorithms, and a knowledge-distilled mini language model to recommend font pairs, achieving scalability for over 3000 fonts and numerous user intents.
One of the key challenges of AI generated designs in Microsoft Designer is selecting the most contextually relevant and novel fonts for the design suggestions. Previous efforts involved manually mapping design intent to fonts. Though this was high quality, this method does not scale for a large number of fonts (3000+) and numerous user intents for graphic design. In this work we create font visual embeddings, a font stroke width algorithm, a font category to font mapping dataset, an LLM-based category utilization description and a lightweight, low latency knowledge-distilled mini language model (Mini LM V2) to recommend multiple pairs of contextual heading and subheading fonts for beautiful and intuitive designs. We also utilize a weighted scoring mechanism, nearest neighbor approach and stratified sampling to rank the font pairs and bring novelty to the predictions.