Automatic article extraction in old newspapers digitized collections

Abstract

We present a complete method for article segmentation in old newspapers, which deals with complex layouts analysis of degraded documents. The designed workflow can process large amounts of documents and generates digital objects in METS/ALTO format in order to facilitate the indexing and the browsing of information in digital libraries. The analysis of the document image is performed by a two stages scheme. Pixels are labeled in a first stage with a Conditional Random Field model in order to intent to label the areas of interest with a low logical level. Then this first logical representation of the document content is analyzed in a second stage to get a higher logical representation including article segmentation and reading order. This top-level structural analysis relies on the generation of an article separation grid applied recursively on the document image, allowing analyzing any type of Manhattan page layout, even for complex structures with multiple columns and overlapping entities. This method which benefits from both a local analysis using a probabilistic model trained using machine learning procedures, and a more global structural analysis using recursive rules, is evaluated on a dataset of daily local press document images covering several time periods and different page layouts, to prove its effectiveness.