Document Parsing Unveiled: Techniques, Challenges, and Prospects for
  Structured Information Extraction
Authors: name: Qintong Zhang, name: Victor Shea-Jay Huang, name: Bin Wang, name: Junyuan Zhang, name: Zhengren Wang, name: Hao Liang, name: Shawn Wang, name: Matthieu Lin, name: Wentao Zhang, name: Conghui He

Document parsing is essential for converting unstructured and semi-structured
documents-such as contracts, academic papers, and invoices-into structured,
machine-readable data. Document parsing extract reliable structured data from
unstructured inputs, providing huge convenience for numerous applications.
Especially with recent achievements in Large Language Models, document parsing
plays an indispensable role in both knowledge base construction and training
data generation. This survey presents a comprehensive review of the current
state of document parsing, covering key methodologies, from modular pipeline
systems to end-to-end models driven by large vision-language models. Core
components such as layout detection, content extraction (including text,
tables, and mathematical expressions), and multi-modal data integration are
examined in detail. Additionally, this paper discusses the challenges faced by
modular document parsing systems and vision-language models in handling complex
layouts, integrating multiple modules, and recognizing high-density text. It
emphasizes the importance of developing larger and more diverse datasets and
outlines future research directions.