Parsing Electronic Theses and Dissertations Using Object Detection (2022)

Abstract: 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. Our code and pre-trained models are available at: https://github.com/Opening-ETDs/ETD-OD .

## introduction long scholarly documents like electronic theses and dissertations (etds) contain a vast amount of information which can be of immense value to the scholarly community. millions of etds are now publicly available on the web, and can serve as a rich source of scholarly information. however, relative to the large amount of information in such documents, a significant portion remains untapped. part of the problem is that these documents are often long and filled with highly specialized details. this makes it difficult for many users to understand the information contained in etds. in recent years, advances have been been made in nlp-based techniques such as question-answering and text summarization, which might be incorporated to make etds more accessible. however, a majority of these documents exist as pdf files. while some tools can work with these files, the results we have observed have been poor; other tools require data in a structured format such as xml. this leads to the research question: is there a way to identify, parse, and extract the information from a pdf version of an etd so that it is more accessible to a wider audience? many research challenges arise when transforming etds from pdf to other formats. these scholarly documents do not have a standard layout. different institutions have their own layouts and formats, making rule-based parsing methods difficult to apply. moreover, the structure and organization of elements present in documents varies by domain and organization. for instance, documents from domains such as mathematics often contain equations, while documents from computer science frequently contain algorithms. hence, there is a need to develop machine-learning based documentparsing methods that can generalize to documents with different layouts and across domains. in recent years, with the advances in the field of computer vision, several methods have been proposed for extracting important elements from documents. some of these approaches perform document layout analysis using object detection models @xcite . however, many of these works and the associated datasets are focused on a very narrow set of scholarly document elements. for instance, tablebank @xcite only contains bounding boxes for tables, while @xcite focuses on figures (and some tables). more recently, there has been research that primarily focuses on layout analysis of scholarly documents @xcite . however, most existing work in the domain of scholarly document understanding focuses on research papers, which differ in many ways from longer documents like theses and dissertations. first, research papers tend to be shorter in length and have a narrower scope. as such, many elements such as chapters, committee, and university that are important in an etd cannot be found in datasets derived from research papers. moreover, research papers significantly differ from etds in their structure and format. for instance, many research papers are in double-column format, while etds typically have a single column, and have bigger font size and spacing. consequently, existing methods for document understanding for research papers are not easily adapted to etds. in this work, we propose etd-od, an object detection based framework to parse long pdf documents such as etds. our approach works on the pdf version of an etd by first identifying important elements such as figures, tables, captions, paragraphs, delimiters like chapter and section headers, and metadata such as title, author name, etc. this is done using object detection models such as faster- @xcite or yolov7 @xcite on individual page images. for textual elements such as paragraphs and captions, the textual content is further extracted using pdf-based tools such as pymupdf, or optical character recognition (ocr). finally, we put together all these elements in a structured xml format. we also introduce a new object detection dataset that contains over 25k page images originating from 200k etds, consisting of elements that commonly occur in etds, that can be important sources of information. an xml schema to support parsing with such objects is also introduced. ## proposed framework we now introduce the proposed framework for transforming long pdf documents into structured xml format. the architecture of our framework is illustrated in figure 2 . the different modules shown can broadly be divided into the following three categories. ## object detection training we use the etd-od dataset introduced in this paper for training object detection models for our framework. the models currently supported are: • faster- @xcite : faster-rcnn is an object detection model that has two stages. a region proposal network generates regions of interest, which are fed to another network for final detection. we use the version of faster-rcnn that uses resnext-101 @xcite as the backbone model. ## experiments in this section, we discuss the results obtained in the experimental analysis of our work. ## conclusion and future work in this work, we presented a new dataset and a framework for parsing long scholarly documents such as etds from pdf to structured formats such as xml. we also presented a schema to represent etds in xml format, along with extensive experimental evaluation of multiple state-of-the-art models on the newly introduced etd-od dataset. in the future, we plan to extend this work to other types of documents, such as old archival documents which typically contain a great amount of noise, and make further improvements to the performance of minority categories.