Scholarly publishing increasingly requires automated systems that semantically enrich documents in order to support management and quality assessment of scientific output. However, contextual information, such as the authors’ affiliations, references, and funding agencies, is typically hidden within PDF files. To access this information we have developed a processing pipeline that analyses the structure of a PDF document incorporating a diverse set of machine learning techniques. First, unsupervised learning is used to extract contiguous text blocks from the raw character stream as the basic logical units of the article. Next, supervised learning is employed to classify blocks into different meta-data categories, including authors and affiliations. Then, a set of heuristics are applied to detect the reference section at the end of the paper and segment it into individual reference strings. Sequence classification is then utilised to categorise the tokens of individual references to obtain information such as the journal and the year of the reference. Finally, we make use of named entity recognition techniques to extract references to research grants, funding agencies, and EU projects. Our system is modular in nature. Some parts rely on models learnt on training data, and the overall performance scales with the quality of these data sets.