A Heuristic Baseline Method for Metadata Extraction from Scanned Electronic Theses and Dissertations
Published in ACM/IEEE Joint Conference on Digital Libraries in 2020, 2020
Recommended citation: Muntabir Hasan Choudhury, Jian Wu, William A. Ingram, and Edward A. Fox. 2020. A Heuristic Baseline Method for Metadata Extraction from Scanned Electronic Theses and Dissertations. In JCDL ’20: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020, Virtual Event, China, August 1-5, 2020. ACM, 515–516. https://doi.org/10.1145/3383583.3398590 https://dl.acm.org/doi/10.1145/3383583.3398590
Extracting metadata from scholarly papers is an important text mining problem. Widely used open-source tools such as GROBID are designed for born-digital scholarly papers but often fail for scanned documents, such as Electronic Theses and Dissertations (ETDs). Here we present a preliminary baseline work with a heuristic model to extract metadata from the cover pages of scanned ETDs. The process started with converting scanned pages into images and then text files by applying OCR tools. Then a series of carefully designed regular expressions for each field is applied, capturing patterns for seven metadata fields: titles, authors, years, degrees, academic programs, institutions, and advisors. The method is evaluated on a ground truth dataset comprised of rectified metadata provided by the Virginia Tech and MIT libraries. Our heuristic method achieves an accuracy of up to 97% on the fields of the ETD text files. Our method poses a strong baseline for machine learning based methods. To our best knowledge, this is the first work attempting to extract metadata from non-born-digital ETDs.
Recommended citation: Choudhury, Muntabir Hasan and Wu, Jian and Ingram, William A. and Fox, Edward A.. “A Heuristic Baseline Method for Metadata Extraction from Scanned Electronic Theses and Dissertations” Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020.