A Heuristic Baseline Method for Metadata Extraction from Scanned Electronic Theses and Dissertations
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.
Muntabir Hasan Choudhury, Jian Wu, William A. Ingram , Edward A. Fox: A Heuristic Baseline Method for Metadata Extraction from Scanned Electronic Theses and Dissertations. JCDL 2020: 515-516
- Date of publication:
- August 1, 2020
- ACM/IEEE on Joint Conference on Digital Libraries
- Page number(s):