  With the rapid development of the internet in the past decade, it has become
increasingly important to extract valuable information from vast resources
efficiently, which is crucial for establishing a comprehensive digital
ecosystem, particularly in the context of research surveys and comprehension.
The foundation of these tasks focuses on accurate extraction and deep mining of
data from scientific documents, which are essential for building a robust data
infrastructure. However, parsing raw data or extracting data from complex
scientific documents have been ongoing challenges. Current data extraction
methods for scientific documents typically use rule-based (RB) or machine
learning (ML) approaches. However, using rule-based methods can incur high
coding costs for articles with intricate typesetting. Conversely, relying
solely on machine learning methods necessitates annotation work for complex
content types within the scientific document, which can be costly.
Additionally, few studies have thoroughly defined and explored the hierarchical
layout within scientific documents. The lack of a comprehensive definition of
the internal structure and elements of the documents indirectly impacts the
accuracy of text classification and object recognition tasks. From the
perspective of analyzing the standard layout and typesetting used in the
specified publication, we propose a new document layout analysis framework
called CTBR(Compartment & Text Blocks Refinement). Firstly, we define
scientific documents into hierarchical divisions: base domain, compartment, and
text blocks. Next, we conduct an in-depth exploration and classification of the
meanings of text blocks. Finally, we utilize the results of text block
classification to implement object recognition within scientific documents
based on rule-based compartment segmentation.
