Mao, Chenyu2025-03-252025-03-252025-03-24vt_gsexam:42536https://hdl.handle.net/10919/125076Document layout understanding often utilizes object detection to locate and parse document elements, enabling systems that convert documents into searchable and editable formats to enhance accessibility and usability. Nevertheless, the recognition results often contain errors that require manual correction due to small training dataset size, limitations of models, and defects in training annotations. However, many of these problems can be addressed via human review to improve correctness. We first improved our system by combining the previous Electronic Thesis/Dissertation (ETD) parsing tool and AI-aided annotation tool, providing instant and accurate file output. Then we used our new pipeline to investigate the effectiveness and efficiency of manual correction strategies in improving object detection accuracy through user studies, including 8 participants, comprising a balanced number of four STEM and four non-STEM researchers, all with some background in ETDs. Each participant was assigned correction tasks on a set of ETDs from both STEM and non-STEM disciplines to ensure comprehensive evaluation across different document types. We collected quantitative metrics, such as completion times, accuracy rates, number of wrong labels, and feedback through our post-survey, to assess the usability and performance of the manual correction process and to examine their relationship with users' academic backgrounds. Results demonstrate that manual adjustment significantly enhanced the accuracy of document element identification and classification, with experienced participants achieving superior correction precision. Furthermore, usability feedback revealed a strong correlation between user satisfaction and system design, providing valuable insights for future system enhancement and development.ETDenIn CopyrightETDdeep learningobject detectiondocument layout analysisThesis