Probabilistic Indexing for Information Search and Retrieval in Large Collections of Handwritten Text Images

Alejandro Héctor Toselli, is currently working as a PostDoc (María Zambrano grant) at the Universitat Politècnica de València. He obtained an Electrical Engineer degree from the University Nacional de Tucumán (Argentina, 1997) and a Phd in Computer Science from the Universitat Politècnica de València (UPV) (Spain, 2004). His research expertise focuses primarily on Document Analysis and Recognition, in which he has more than 20 years of experience, publishing on these topics and working on related projects funded by European and US institutions. He held a Post-Doctoral Fellow at Northeastern University (Boston, USA) in the the multi-institutional Open Islamicate Texts Initiative (OpenITI) and at the "Institut de Recherche en Informatique et Systèmes Aléatoires" (IRISA, Rennes France).Joan Puigcerver received his MSc and PhD in Computer Science from the Universitat Politècnica de València, in 2014 and 2018, respectively, focusing on probabilistic indexing and handwritten text recognition. In 2018, he joined Google Research as a software engineer. His research focuses on deep learning architectures, transfer learning, and computer vision. Joan is a member of the Spanish Society for Pattern Recognition and Image Analysis (AERFAI), an affiliate organization of the International Association for Pattern Recognition (IAPR).Enrique Vidal is an emeritus professor of the Universitat Politècnica de València (Spain) and former co-leader of the PRHLT research center there. He is co-author of hundreds of research papers in the fields of Pattern Recognition, Multimodal Interaction and applications to Language, Speech and Image Processing and has led many important projects in these fields. Enrique is a fellow of the International Association for Pattern Recognition (IAPR).

• Preface.- Acronyms.- Introduction.- State of the Art.-  Probabilistic Indexing (PrIx) Framework .-  Probabilistic Models for Handwritten Text .- Probabilistic Indexing for Fast and Effective Information Retrieval. -  Empirical Validation of Probabilistic Indexing Methods. -  Conclusion and Outlook .- Appendices.