Recommendation systems in mathematical character recognition

In handwritten text there are usually several accepted styles for forming each character. We hypothesize that in the handwriting of individuals there is a correlation among the styles used for characters, and that these correlations may be used to anticipate which styles particular writers will use for symbols that have not yet been seen. This approach may prove useful in the setting of mathematical handwriting recognition, where there are many symbols and it would be onerous to require writers to provide samples of every one in order to personalize handwriting recognition. We describe preliminary experiments using ideas from the area of recommendation systems to predict which styles writers will likely use for symbols they have not yet written. The experiments demonstrate that writers tend to use only a small fraction of the possible combinations of character writing styles, and there are correlations among the styles used for symbols.