Template-Based Document Information Extraction Using Neural Network Keypoint Filtering
Date of Award
8-1-2024
Degree Name
M.S. in Electrical Engineering
Department
Department of Electrical and Computer Engineering
Advisor/Chair
Russell Hardie
Abstract
Documents like invoices, receipts, and forms are essential to many modern business operations. We develop a system for autonomously processing common United States Air Force contract front forms. The system takes in a form and extracts a key-value pair for each box in the form. This task is called key information extraction. In a structured document, the layout is the same from instance to instance (perhaps allowing for rigid transforms). Our documents are semi-structured because, although their layouts are similar, some of the content may be in slightly different places between instances of the form. This makes information extraction harder because the response regions may be in different places from form to form. We demonstrate that, despite the added difficulty, template matching and registration makes for a strong baseline on our semi-structured forms. Additionally, we propose a filtering approach for keypoints based on their position in the layout. Specifically, we use a trained U-Net model to identify intersections and end-points in the form's "wire-frame.'' Then, the pipeline only uses keypoints that are close to those landmarks. We demonstrate that this method improves the registration quality over our baseline, results in a more intuitive distribution of keypoints across the image, and potentially speeds up processing since fewer keypoints need matching.
Keywords
Deep learning; semantic segmentation; automatic document processing; information extraction
Rights Statement
Copyright © 2024, author.
Recommended Citation
Flaute, Dylan M., "Template-Based Document Information Extraction Using Neural Network Keypoint Filtering" (2024). Graduate Theses and Dissertations. 7406.
https://ecommons.udayton.edu/graduate_theses/7406
