March 2019 – present

Jean-Christophe BURIE is Professor in Computer Science at La Rochelle Université and member of the L3i research laboratory in La Rochelle, France. He has a broad experience in Image Processing, Image Understanding, Pattern Recognition and especially in Document Analysis and Graphic Recognition. Over the years, his research topics have ranged from Maps Analysis, Comics Understanding, Ancients manuscripts Indexing.
He served International Association for Pattern Recognition TC-10 Committee as Secretary and Webmaster from 2009 to 2018, as vice-chair from 2019 to 2021 and now as chair. He has authored over 100 publications, most of which in various high rank journals and conferences. He was the co-organizer of several workshop such as MANPU 2016 (Cancun, Mexico), MANPU 2017 and HDI 2017 (Kyoto, Japan), MANPU 2019 (Thessalonique, Greece). He was the General Chair of the 2nd IAPR TC10/TC11 Summer School in La Rochelle (July 2018), GREC Workshop in 2019 (Sydney, Australia) and in 2021 (Lausanne, Switzerland). For more information, please visit the webpage.

Vice Chair
Miki Ueno, Osaka Institute of Technology, Japan. For more information, please visit the webpage.

Communications Officer
Dr. Christophe RIGAUD (Ph.D.) is a research engineer (postdoc) in document image analysis at the L3i laboratory of the University of La Rochelle (France). He received a double European PhD degree in computer science from the University of La Rochelle (France) and the Autonomous University of Barcelona (Spain) in 2014. His PhD thesis is titled “Segmentation and indexation of complex objects in comic book images”, supervised by Jean-Christophe Burie, Jean-Marc Ogier from L3i lab and Dimosthenis Karatzas from the Center of Computer Vision (CVC) of the Autonomous University of Barcelona. His current research interest is the analysis of comic book images using computer vision techniques. He aims to discover how to make a complete and automatic description of the comic page image content, namely the position of the panels, speech balloons, text, comic characters, their interactions and semantic meaning. Christophe Rigaud has authored 20 scientific publications including 5 book chapters, 3 journals and several international conference and workshop papers. He also serves several program committees of summer school (SSDA’18), workshops (GREC’17, MANPU’17) and competitions such as the Engineering Drawing Challenge for GREC’15, Multi-lingual scene text detection and script identification (RRC-MLT) at ICDAR’17, Subgraph Spotting in Graph-based representations of Comic Images (SSGCI) for ICPR’18, Fine-Grained Classification of Comic Characters (FGC) and Post-OCR Text Correction (POCR) at ICDAR’19. For more information, please visit the webpage.

Educational Officer
KC Santosh (PhD) is the Chair and Associate Professor of the Department of Computer Science (CS) at the University of South Dakota (USD). Dr. Santosh served School of Computing and IT, Taylor’s University as a Visiting Associate Professor, for one year (2019/2020). Before joining USD, Dr. Santosh worked as a research fellow at the U.S. National Library of Medicine (NLM), National Institutes of Health (NIH). He worked as a postdoctoral research scientist at the LORIA research centre, Universite de Lorraine in direct collaboration with industrial partner ITESOFT, France. He also served as a research scientist at the INRIA Nancy Grand Est research centre, France, where, he has received his PhD diploma in Computer Science. Before that, he worked as a graduate research scholar at the SIIT, Thammasat University, Thailand. Dr. Santosh has demonstrated expertise in artificial intelligence, machine learning, pattern recognition, computer vision, image processing, data mining and big data with various application domains, such as healthcare informatics and medical imaging, document imaging, biometrics, forensics, speech analysis and Internet of Things. As of now (Aug. 2020), Dr. Santosh published more than 180 research works that include journal articles (70), conference proceedings (100) and book chapters (11). He authored two books, and edited five books, 14 journal issues and six conference proceedings. Dr. Santosh serves as editor-in-chief for the International Journal of Signal and Image Processing and associate editor for multiple journals, such as the International Journal of Machine Learning & Cybernetics, Springer Nature and IEEE Access. Dr. Santosh chaired more than 10 international conference events in the domain. His research projects are funded by multiple agencies, such as SDCRGP, Department of Education (DOE) and National Science Foundation (NSF). Dr. Santosh is the proud recipient of the President’s Research Excellence Award (USD, 2019) and an award from the U.S. Department of Health & Human Services (2014). For more information, please visit the webpage.

Dataset Curator
Dr. Pau Riba received his B.Sc. in Mathematics and Computer Science and his M.Sc. in Computer Vision from the Universitat Autònoma de Barcelona. In addition, he also received his Ph.D. degree in 2020 from the Universitat Autònoma de Barcelona. Currently, he is a postdoctoral researcher at the Computer Vision Center. Over the years, he has published more than 20 research articles in international conferences and journals. His main research interests include Deep Learning, Structural Pattern Recognition and Document Image Analysis. For more information, please visit the webpage.

Muhammad Muzzamil LUQMAN (Ph.D.) is a research scientist in Document Image Analysis, Pattern Recognition and Computer Vision. Luqman is currently working on the post of Research Engineer (Permanent) at the L3i Laboratory, University of La Rochelle (France). Luqman has a PhD in Computer Science from François Rabelais University of Tours (France) and Autonoma University of Barcelona (Spain). His PhD thesis was co-supervised by Professor Jean-Yves Ramel and Professor Josep Llados; and was titled “Fuzzy Multilevel Graph Embedding for Recognition, Indexing and Retrieval of Graphic Document Images”. His research interests include Structural Pattern Recognition, Document Image Analysis, Camera-Based Document Analysis and Recognition, Graphics Recognition, Machine Learning, Computer Vision, Augmented Reality and Biomimicry. For more information, please visit the webpage.