The following persons will organize this competition:
Contact: firstname.lastname@example.org, www5.cs.fau.de/~christlein
Short-bio: He received his Diploma degree in computer science in July 2012 from the Friedrich-Alexander University of Erlangen-Nürnberg, Germany. During his studies, he worked on the detection of copy-move forgeries in the field of image forensics. Currently, he is pursuing his PHD-studies in the analysis of handwritings with focus on writer identification and writer retrieval. His research interests lie in the field of computer vision and pattern recognition, particularly in handwriting analysis and historical document analysis.
Experience: He participated in various competitions on script type classification and writer recognition between 2014 and 2018. Last year, he co-organized the “ICDAR2017 Competition on Historical Document Writer Identification (Historical-WI)”.
Contact: email@example.com, www.irht.cnrs.fr/annuaire/stutzmann-dominique
Short-bio: After degrees in Classics, History and German studies at the Sorbonne, he studied at the Ecole Nationale des Chartes (2002), received a MLIS and worked at the Staatsbibliothek zu Berlin and the Bibliothèque nationale de France. He received his PhD in history in 2009 from the Université Paris-1 Panthéon Sorbonne, France. He completed a PhD on scribal practices in medieval communities. He is senior researcher at the Institut de Recherche et d’Histoire des Textes (CNRS) and Principal Investigator of several research projects in the field of digital humanities and palaeography.
Experience: He co-organized two sessions of the “Competition on the Classification of Medieval Handwritings in Latin Script”, including script type classification and dating of handwriting samples at ICFHR2016 and ICDAR2017.
Contact: firstname.lastname@example.org, www5.cs.fau.de/~nicolaou
Short-bio: Anguelos Nicolaou obtained his master degree in 2014 in computer science from the university of Bern, Switzerland. He is a PhD student in the Computer Vision Center at the Autonomous University of Barcelona studding robust reading systems. He is currently working on a project involving information retrieval from historical document images from the Czech Republic.
He co-organized the “ICDAR2015 Robust Reading Competition”.
Contact: email@example.com, www5.cs.fau.de/~seuret
Short-bio: He obtained his Master degree in computer science at the University of Fribourg, Switzerland in 2013. He is currently a PhD student in the Document, Image and Voice Analysis (Diva) research group of the same university, with a main focus on convolutional neural networks applied on layout analysis of historical document images. He recently started working, in parallel to his studies, on font analysis, classification and clustering of printed historical books at the Friedrich-Alexander University of Erlangen-Nürnberg, Germany.
Experience: He co-organized the “ICDAR2017 Competition on Layout Analysis for Challenging Medieval Manuscripts”.
Contact: firstname.lastname@example.org, www5.cs.fau.de/~maier
Short-bio: Andreas Maier studied Computer Science, graduated in 2005, and received his PhD in 2009. From 2005 to 2009 he was working at the Pattern Recognition Lab at the Computer Science Department of the University of ErlangenNuremberg. His major research subject was medical signal processing in speech data. In this period, he developed the first online speech intelligibility assessment tool – PEAKS – that has been used to analyze over 4.000 patient and control subjects so far. From 2009 to 2010, he started working on flat-panel C-arm CT as post-doctoral fellow at the Radiological Sciences Laboratory in the Department of Radiology at the Stanford University. From 2011 to 2012 he joined Siemens Healthcare as innovation project manager and was responsible for reconstruction topics in the Angiography and X-ray business unit. In 2012, he returned the University of Erlangen-Nuremberg as head of the Medical Reconstruction Group at the Pattern Recognition lab. In 2015 he became professor and head of the Pattern Recognition Lab.
Experience: He has supervised and instructed PhD students in historical document processing. Highlights include Vincent Christlein’s writer identification and Daniel Stromer’s scanning of historial books without opening them.