OCR++: A Robust Framework For Information Extraction from Scholarly Articles
Mayank Singh | Barnopriyo Barua | Priyank Palod | Manvi Garg | Sidhartha Satapathy | Samuel Bushi | Kumar Ayush | Krishna Sai Rohith | Tulasi Gamidi | Pawan Goyal | Animesh Mukherjee
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
This paper proposes OCR++, an open-source framework designed for a variety of information extraction tasks from scholarly articles including metadata (title, author names, affiliation and e-mail), structure (section headings and body text, table and figure headings, URLs and footnotes) and bibliography (citation instances and references). We analyze a diverse set of scientific articles written in English to understand generic writing patterns and formulate rules to develop this hybrid framework. Extensive evaluations show that the proposed framework outperforms the existing state-of-the-art tools by a large margin in structural information extraction along with improved performance in metadata and bibliography extraction tasks, both in terms of accuracy (around 50% improvement) and processing time (around 52% improvement). A user experience study conducted with the help of 30 researchers reveals that the researchers found this system to be very helpful. As an additional objective, we discuss two novel use cases including automatically extracting links to public datasets from the proceedings, which would further accelerate the advancement in digital libraries. The result of the framework can be exported as a whole into structured TEI-encoded documents. Our framework is accessible online at http://www.cnergres.iitkgp.ac.in/OCR++/home/.
- Mayank Singh 1
- Barnopriyo Barua 1
- Priyank Palod 1
- Manvi Garg 1
- Samuel Bushi 1
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