NewsEye will develop a seamlessly integrated armoury of tools and methods will be created that will improve users’ capability to access, analyse and use the content in the digital Libraries of historical newspapers. NewsEye will thus seek to improve existing tools on (amongst others):

Text Recognition and Article Separation: NewsEye will essentially address two major obstacles of current research projects dealing with historical newspapers: One is the fact that in many cases, conventional Optical Character Recognition (OCR) does not provide satisfying results. The other is that text recognition results are mostly on newspaper page level only instead on the appropriate article level.

Multilingual and Uncertainty-aware Semantic Text Enrichment: While named entity recognition (NER) and linking (NEL) are very active research areas, their results still are very weak when applied to historical data. The main reason is that most of the models require linguistic analysis, which is not robust to noisy text recognition.

Dynamic Text Analysis: Tools for exploring large sets of historical newspapers are scarce, in particular in terms of advanced ability to discover and express historical trends, topics and viewpoints suggested by large-scale analysis.