Transformers and Language Models in Form Understanding: A Comprehensive
  Review of Scanned Document Analysis
Abdelrahman Abdallah Daniel Eberharter Zoe Pfister Adam Jatowt
This paper presents a comprehensive survey of research works on the topic of
form understanding in the context of scanned documents. We delve into recent
advancements and breakthroughs in the field, highlighting the significance of
language models and transformers in solving this challenging task. Our research
methodology involves an in-depth analysis of popular documents and forms of
understanding of trends over the last decade, enabling us to offer valuable
insights into the evolution of this domain. Focusing on cutting-edge models, we
showcase how transformers have propelled the field forward, revolutionizing
form-understanding techniques. Our exploration includes an extensive
examination of state-of-the-art language models designed to effectively tackle
the complexities of noisy scanned documents. Furthermore, we present an
overview of the latest and most relevant datasets, which serve as essential
benchmarks for evaluating the performance of selected models. By comparing and
contrasting the capabilities of these models, we aim to provide researchers and
practitioners with useful guidance in choosing the most suitable solutions for
their specific form understanding tasks.