3.3.5. Multimodal

The process of font generation through multimodal representation might be likened to a chef who relies not merely on photographs or recipes in isolation but rather synthesises multiple sources of information – visual, procedural, and experiential – to achieve the desired result.

Multimodal representation, in technical terms, comprises the encoding of multiple data modalities into tensor formats suitable for computational processing. The capture of semantic information proves particularly crucial in this context, as it enables machine learning models to leverage complementary features across heterogeneous data sources. This approach facilitates the exploitation of synergies between modalities, though the inherent complexity of feature extraction from dissimilar data types presents notable challenges.

One might be tempted to conclude that an increased number of modalities invariably yields superior results – but does this assumption withstand scrutiny?

In contemporary font generation research, the predominant modalities encompass spatial and sequential representations. The spatial modality captures the visual structure and geometric properties, whilst the sequential modality encodes the temporal aspects of glyph construction. These modalities are frequently combined in spatio-sequential approaches. However, the fusion of these modalities need not follow a singular methodology – various architectural strategies for modality integration have been explored in the literature.