Abstract
This paper introduces a novel system for information extraction from visually rich documents (VRD) using a weighted graph representation. The proposed method aims to improve the performance of the information extraction task by capturing the relationships between various VRD components. The VRD is modeled as a weighted graph, in which visual, textual, and spatial features of text regions are encoded in nodes and edges representing the relationships between neighboring text regions. The information extraction task from VRD is performed as a node classification task through the use of a graph convolutional networks, where the VRD graphs are fed into the network. The approach is evaluated across diverse documents, encompassing invoices and receipts, revealing achievement levels equal to or surpassing robust baselines.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
