Josep Lladós received the degree in Computer Sciences in 1991 from the Universitat Politècnica de Catalunya and the PhD degree in Computer Sciences in 1997 from the Universitat Autònoma de Barcelona (Spain) and the Université Paris 8 (France). Currently he is an Associate Professor at the Computer Sciences Department of the Universitat Autònoma de Barcelona and a staff researcher of the Computer Vision Center, where he is also the director since January 2009. He is associate researcher of the IDAKS Lab of the Osaka Prefecture University (Japan). He is chair holder of Knowledge Transfer of the UAB Research Park and Santander Bank. He is the coordinator of the Pattern Recognition and Document Analysis Group (2014SGR-1436). His current research fields are document analysis, structural and syntactic pattern recognition and computer vision. He has been the head of a number of Computer Vision R+D projects and published more than 230 papers in national and international conferences and journals, and supervised 12 PhD theses. J. Lladós is an active member of the Image Analysis and Pattern Recognition Spanish Association (AERFAI), a member society of the IAPR. He is currently the chairman of the IAPR-EC (Education Committee). Formerly he served as chairman of the IAPR-ILC (Industrial Liaison Committee), the IAPR TC-10, the Technical Committee on Graphics Recognition, and also he is a member of the IAPR TC-2 (Structural Pattern Recognition), IAPR TC-11 (Reading Systems) and IAPR TC-15 (Graph based Representations). He is chief editor of the ELCVIA (Electronic Letters on Computer Vision and Image Analysis). He is co-Editor in Series in Machine Perception and Artificial Intelligence (SMPAI) of World Scientfic Publishing Company. He serves on the Editorial Board of the Pattern Recognition journal, in IJDAR (International Journal in Document Analysis and Recognition), the Frontiers in Digital Humanities journal, and also a PC member of a number of international conferences. He was the recipient of the IAPR-ICDAR Young Investigator Award in 2007. He was the general chair of the International Conference on Document Analysis and Recognition (ICDAR’2009) held in Barcelona in July 2009, and co-chair of the IAPR TC-10 Graphics Recognition Workshop of 2003 (Barcelona), 2005 (Hong Kong), 2007 (Curitiba) and 2009 (La Rochelle). Josep Lladós has also experience in technological transfer and in 2002 he created the company ICAR Vision Systems, a spin-off of the Computer Vision Center working on Document Image Analysis, after winning the entrepreneur award from the Catalonia Government on business projects on Information Society Technologies in 2000.
Archival manuscripts are a fundamental heritage of our culture that must be transmitted in the best conditions to future generations. Although being a repository of human knowledge throughout the ages, archival documents are rarely used because of the difficulty to access. Population records (birth, marriage, death, censuses), most of them digitized and residing in historical archives, are a unique, reliable and homogeneous heritage of the past, reflecting the evolving collective memory of societies. The progress in the field of document image analysis and recognition, has resulted in efficient and scalable reading systems for historical handwritten document images. However, pure transcription is not enough for a real understanding of the document contents, and the inclusion of semantic and contextual knowledge is necessary to narrow the semantic gap.
Recent developments in computer vision for handwritten text recognition and machine learning capabilities have resulted in efficient and scalable reading systems able to extract meaning from images. Scholars from fields ranging from demography to anthropology or health will be able to face a more sophisticated analysis of the data, including linkage of families across time and space, or the identification of causal relations.
In this talk we will present our recent achievement in the interpretation of historical handwritten document images. Large scale HTR methods and query-by-example and query-by-string word spotting methods allow extracting information from document images. In addition, this will be enriched with the oral and digital cultural heritage materials provided by citizens, as “natural archives”, engaged in crowsourcing experiences. We propose the paradigm of social networks as a viable representation of the digital heritage of the communities of the past, while connecting it with the communities of the present. As a metaphor of the lifelogging of the past, the navigation through the network will provide chronicles of vital processes that unfold within individual life courses, events, cultural artifacts (pictures, recordings), etc. New transmedia experiences can be proposed on top of this architecture as innovative services based on the mining of the historical data.