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The forensic attribution of the handwriting in a digitized document tomultiple scribes is a challenging problem of high dimensionality. Uniquehandwriting styles may be dissimilar in a blend of several factors includingcharacter size, stroke width, loops, ductus, slant angles, and cursiveligatures. Previous work on labeled data with Hidden Markov models, supportvector machines, and semi-supervised recurrent neural networks have providedmoderate to high success. In this study, we successfully detect hand shifts ina historical manuscript through fuzzy soft clustering in combination withlinear principal component analysis. This advance demonstrates the successfuldeployment of unsupervised methods for writer attribution of historicaldocuments and forensic document analysis.

Recognizing Handwriting Styles in a Historical Scanned Document Using Unsupervised Fuzzy Clustering