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Author Wahlberg, F. ♦ Martensson, L. ♦ Brun, A.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2014
Language English
Subject Domain (in DDC) Technology ♦ Engineering & allied operations ♦ Other branches of engineering
Subject Keyword Histograms ♦ Measurement ♦ Equations ♦ Training data ♦ Additives ♦ Training ♦ historical manuscripts ♦ writer identification ♦ semi-supervised learning ♦ classification
Abstract In this paper, we propose a novel pipeline for automated scribal attribution based on the Quill feature: 1) We compensate the Quill feature histogram for pen changes and page warping. 2) We add curvature as a third dimension in the feature histogram, to better separate characteristics like loops and lines. 3) We also investigate the use of several dissimilarity measures between the feature histograms. 4) We propose and evaluate semi-supervised learning for classification, to reduce the need of labeled samples. Our evaluation is performed on 1104 pages from a 15th century Swedish manuscript. It was chosen because it represents a significant part of Swedish manuscripts of said period. Our results show that only a few percent of the material need labelling for average precisions above 95%. Our novel curvature and registration extensions, together with semi-supervised learning, outperformed the current Quill feature.
ISBN 9781479943357
ISSN 21676445
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2014-09-01
Publisher Place Greece
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9781479943340
Size (in Bytes) 285.41 kB
Page Count 6
Starting Page 732
Ending Page 737


Source: IEEE Xplore Digital Library