Scribal Attribution using a Novel 3-D Quill-Curvature Feature Histogram
2014 (English)In: Proceedings International Conference on Frontiers in Handwriting Recognition (ICFHR), 2014, 2014Conference paper (Refereed)
In this paper, we propose a novel pipeline forautomated scribal attribution based on the Quill feature: 1) Wecompensate the Quill feature histogram for pen changes andpage warping. 2) We add curvature as a third dimension in thefeature histogram, to better separate characteristics like loopsand lines. 3) We also investigate the use of several dissimilaritymeasures between the feature histograms. 4) We propose andevaluate semi-supervised learning for classification, to reducethe need of labeled samples.Our evaluation is performed on 1104 pages from a 15thcentury Swedish manuscript. It was chosen because it repre-sents a significant part of Swedish manuscripts of said period.Our results show that only a few percent of the materialneed labelling for average precisions above 95%. Our novelcurvature and registration extensions, together with semi-supervised learning, outperformed the current Quill feature.
Place, publisher, year, edition, pages
writer identification; semi-supervised learning; classification; historical manuscripts
Research subject Computer Science
IdentifiersURN: urn:nbn:se:uu:diva-238270OAI: oai:DiVA.org:uu-238270DiVA: diva2:770693
The International Conference on Frontiers in Handwriting Recognition (ICFHR), September 1-4, 2014, Crete, Greece