We show that it outperforms a state-of-the-art system in terms of the quality of the extracted body text and table of contents. We separately evaluate the individual stages of our pipeline on a number of different datasets and compare it with other document structure analysis approaches. Based on this resulting logical structure we finally extract the body text and the table of contents of a scientific article. Next, we determine geometrical relations between these blocks, which, together with geometrical and font information, are then used categorize the blocks into different classes. First, contiguous text blocks are extracted from the raw character stream. Apart from the meta-data extraction, which we reused from previous work, our system uses only information available from the current document and does not require any pre-trained model.
#DOCEAR WRONG ENCODING PDF#
To overcome these challenges, we have developed a processing pipeline that analyses the structure a PDF document using a number of unsupervised machine learning techniques and heuristics.
However, the layout of scientific articles is highly varying across publishers, and common digital document formats are optimised for presentation, but lack structural information. Text mining and information retrieval in large collections of scientific literature require automated processing systems that analyse the documents’ content.
0 kommentar(er)
