Survey on Question Answering over Visually Rich Documents: Methods, Challenges, and Trends
Camille Barboule, Benjamin Piwowarski, Yoan Chabot

TL;DR
This survey reviews current methods, challenges, and trends in question answering over visually-rich documents, highlighting the need for standardized processing pipelines and future research directions.
Contribution
It offers a comprehensive overview of state-of-the-art approaches, analyzing their strengths and limitations, and identifies key challenges and promising research directions in the field.
Findings
Highlights the lack of consensus on processing pipelines
Identifies main challenges in visually-rich document understanding
Proposes promising future research directions
Abstract
The field of visually-rich document understanding, which involves interacting with visually-rich documents (whether scanned or born-digital), is rapidly evolving and still lacks consensus on several key aspects of the processing pipeline. In this work, we provide a comprehensive overview of state-of-the-art approaches, emphasizing their strengths and limitations, pointing out the main challenges in the field, and proposing promising research directions.
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Taxonomy
TopicsMultimodal Machine Learning Applications · Topic Modeling · Text and Document Classification Technologies
