Writer Identification on Historical Manuscripts
In recent years, Automatic Writer Identification (AWI) has received a lot of attention in the document analysis community. However, most research has been conducted on contemporary benchmark sets. These datasets typically do not contain any noise or artefacts caused by the conversion methodology. This article analyses how current state-of-the-art methods in writer identification perform on historical documents. In contrast to contemporary documents, historical data often contain artefacts such as holes, rips, or water stains which make reliable identification error-prone.
Robert Sablatnig was born in Klagenfurt, Carinthia, Austria, in 1965. From 1992 to 2003 he was an assistant professor (Univ.Ass.), and from 2003 to 2010 an associate professor (ao Univ.Prof.) of computer vision at the Pattern Recognition and Image Processing Group. From 2005 to 2017 he was the head of the Institute of Computer Aided Automation. Since 2010 he is heading the Computer Vision Lab, which is part of the newly founded Institute of Visual Computing & Human-Centered Technology (TU Wien), engaged in research, project leading, and teaching. His research interests are 3D Computer Vision including Range Finder, Stereovision, Shape from X, Registration, Calibration, Robot Vision; Automatic Visual Inspection, Hierarchical Pattern Recognition, Video data analysis (Motion and Tracking), Automated Document Analysis, Multispectral Imaging, Virtual- and Augmented Reality, and Applications in Industry and Cultural Heritage Preservation.