Clustering of Symbols Using Minimal Description Length

The clustering of glyphs (individual letters/characters/symbols) is typically the first step in document processing algorithms and a critical enabling technology for most historical document indexing techniques. In this work, we take a step back from current domain/language specialized research efforts to consider the problem from an agnostic perspective. In particular, we claim that, independent of the distance measure used, any method that attempts to cluster all the data is almost certainly doomed to failure. We explain this observation, and introduce a clustering method based on Minimum Description Length (MDL) that can overcome it.