Abstract
Abstract: The increasing demand for effective document information extraction methods has underscored the necessity of addressing challenges related to semi-structured tables and diverse content formats. This survey extensively explores the intricate task of extracting information from documents with a particular emphasis on the challenges associated with precise Key Information Extraction (KIE) and their broader implications for enhancing document understanding efficiency. The survey delves into recent breakthroughs in this domain, with a special focus on notable approaches such as BROS, BloombergGPT, and the innovative Document Understanding Transformer (DonUT). Additionally, it provides a comprehensive analysis of various studies in Key Information Extraction (KIE) and Visual Document Understanding (VDU), elucidating the strengths and weaknesses of these endeavors. It also provides justification for highlighting DonUT lies in its unique OCR-free VDU model architecture based on Transformers, incorporating a pre-training objective that utilizes cross-entropy loss. The survey not only addresses current challenges but also illuminates promising avenues for advancing document text extraction techniques.
Sign-in/Register to access full text options 
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 callMore From: International Journal for Research in Applied Science and Engineering Technology
Disclaimer: 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.
