Recognition of table of contents for electronic library consulting

Abstract.

A labelling approach for the automatic recognition of tables of contents (ToC) is described in this paper. A prototype is used for the electronic consulting of scientific papers in a digital library system named Calliope. This method operates on a roughly structured ASCII file, produced by OCR. The recognition approach operates by text labelling without using any a priori model. Labelling is based on part-of-speech tagging (PoS) which is initiated by a primary labelling of text components using some specific dictionaries. Significant tags are first grouped into homogeneous classes according to their grammar categories and then reduced in canonical forms corresponding to article fields: “title” and “authors”. Non-labelled tokens are integrated in one or another field by either applying PoS correction rules or using a structure model generated from well-detected articles. The designed prototype operates very well on different ToC layouts and character recognition qualities. Without manual intervention, a 96.3% rate of correct segmentation was obtained on 38 journals, including 2,020 articles, accompanied by a 93.0% rate of correct field extraction.