Parsing Electronic Theses and Dissertations Using Object Detection
Aman Ahuja, Alan Devera, Edward A. Fox · 2022
Electronic theses and dissertations (ETDs) contain valuable knowledge that can be useful for a wide range of purposes.To effectively utilize the knowledge contained in ETDs for downstream tasks such as search and retrieval, question-answering, and summarization, the data first needs to be parsed and stored in a format such as XML.However, since most of the ETDs available on the web are PDF documents, parsing them to make their data useful for downstream tasks is a challenge.In this work, we propose a dataset and a framework to help with parsing long scholarly documents such as ETDs.We take the Object Detection approach for document parsing.We first introduce a set of objects that are important elements of an ETD, along with a new dataset ETD-OD that consists of over 25K page images originating from 200 ETDs with bounding boxes around each of the objects.We also propose a framework that utilizes this dataset for converting ETDs to XML, which can further be used for ETD-related downstream tasks.