Article Segmentation in Digitised Newspapers with a 2D Markov Model Document analysis and recognition is increasingly used to digitise collections of historical books, newspapers and other periodicals. In the digital humanities, it is often the goal to apply information retrieval (IR) and natural language processing (NLP) techniques to help researchers analyse and navigate these digitised archives. The lack of article segmentation is impairing many IR and NLP systems, which assume text is split into ordered, errorfree documents. We define a document analysis and image processing task for segmenting digitised newspapers into articles and other content, e.g. adverts, and we automatically create a dataset of 11602 articles. Using this dataset, we develop and evaluate an innovative 2D Markov model that encodes reading order and substantially outperforms the current stateoftheart, reaching similar accuracy to human annotators.

Text Line Segmentation in Historical Document Images Using an Adaptive UNet Architecture On most document image transcription, indexing and retrieval systems, text line segmentation remains one of the most important preliminary task. Hence, the research community working in document image analysis is particularly interested in providing reliable text line segmentation methods. Recently, an increasing interest in using deep learningbased methods has been noted for solving various subfields and tasks related to the issues surrounding document image analysis. Thanks to the computer hardware and software evolution, several methods based on using deep architectures continue to outperform the pattern recognition issues and particularly those related to historical document image analysis. Thus, in this paper we present a novel deep learningbased method for text line segmentation of historical documents. The proposed method is based on using an adaptive UNet architecture. Qualitative and numerical experiments are given using a large number of historical document images collected from the Tunisian national archives and different recent benchmarking datasets provided in the context of ICDAR and ICFHR competitions. Moreover, the results achieved are compared with those obtained using the stateoftheart methods.