35. One or more non-transitory computer readable media storing instructions that are executable by a processing device, and upon such execution cause the processing device to perform operations comprising: attaining data representing features of a font capable of representing one or more glyphs; and determining a rating for pairing the font and at least one other font using a machine learning system and the data representing the features of the font, wherein at least one of the features or at least one category of a set of categories is identified to represent the font by the machine learning using the features of the font, and wherein the machine learning system produces a vector of numerical values, each numerical value represents one of the features or one of the categories in the set of categories.
35. One or more non-transitory computer readable media storing instructions that are executable by a processing device, and upon such execution cause the processing device to perform operations comprising: attaining data representing features of a font capable of representing one or more glyphs; and determining a rating for pairing the font and at least one other font using a machine learning system and the data representing the features of the font, wherein at least one of the features or at least one category of a set of categories is identified to represent the font by the machine learning using the features of the font, and wherein the machine learning system produces a vector of numerical values, each numerical value represents one of the features or one of the categories in the set of categories. 43. The non-transitory computer readable media of claim 35 , wherein the features are derived based upon separating two or more categories.