Many documents can only be accessed through digitization. This is notably the case of historical and handwritten documents, but also that of many digitally-born documents, turned into images for various reasons (e.g., a le conversion or the intermediary use of an analog form in order to manually sign a document, ll out a form, send by post, etc.). Being able to analyze the textual content of such digitized documents requires a phase of conversion from the captured image to a textual representation, key parts of which are optical character recognition (OCR) and layout analysis. The resulting text and structure are often imperfect, to an extent which is notably correlated with the quality of the initial medium (which may be stained, folded, aged, etc.) and with the quality of the image taken from it. In this talk, I will present recent advances in AI and natural language understanding that enable this type of corpus to be analyzed in a way that is robust to digitization. For example, I will show how we were able, in the H2020 NewsEye project to create state-of-the-art results for the cross-lingual recognition and disambiguation of named entities (names of people, places, and organizations) in large corpora of historical newspapers written in 4 languages, written between 1850 and 1950. This type of results paves the way to a large-scale analysis of digitized documents, notably able to cross linguistic borders.
main research interests lie in the elds of information retrieval, natural language processing, (text) data mining and arti cial intelligence. The central focus of his work is on the development of methods that scale to very large document collections and that use as few external resources as possible, in order to be applicable to documents of any type written in any language, from news articles to social networks, and from digitized manuscripts to digitally-born documents. Antoine Doucet holds a PhD in computer science from the University in Helsinki (Finland) since 2005, and a French research supervision habilitation (HDR) since 2012.