The content delves into the advancements in form understanding techniques, emphasizing the role of language models and transformers. It discusses key datasets like FUNSD and XFUND, highlighting challenges and solutions in document analysis.
The review covers various models such as LayoutLM, SelfDoc, and StrucTexTv2, detailing their unique approaches to integrating text, layout, and visual information for improved document understanding. It also examines datasets like RVL-CDIP and IIT-CDIP used for evaluation purposes.
Furthermore, the article addresses early approaches in document understanding, graph-based models, multi-modal fusion models, sequence-to-sequence models, layout representation models, language-independent models, hybrid transformer architectures, and cross-modal interaction models. It provides insights into their methodologies and contributions to the field.
Overall, the comprehensive review offers valuable insights into the evolution of form understanding techniques through the lens of transformers and language models.
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arxiv.org
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by Abdelrahman ... um arxiv.org 03-08-2024
https://arxiv.org/pdf/2403.04080.pdfTiefere Fragen