Scribe Identification in Medieval English Manuscripts

Abstract

In this paper we present work on automated scribe identification on a new Middle-English manuscript dataset from around the 14th -- 15th century. We discuss the image and textual problems encountered in processing historical documents, and demonstrate the effect of accounting for manuscript style on the writer identification rate. The grapheme codebook method is used to achieve a Top-1 classification accuracy of up to 77% with a modification to the distance measure. The performance of the Sparse Multinomial Logistic Regression classifier is compared against five k-nn classifiers. We also consider classification against the principal components and propose a method for visualising the principal component vectors in terms of the original grapheme features.