Document Header Extraction Using Global Coherence and Boilerplate Removal
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Solution Overview
Problem
Existing methods for extracting structure and header information from digital documents are inadequate due to inconsistencies in document formatting, errors introduced during conversion, and the presence of boilerplate text, making it difficult to accurately identify section hierarchies and boundaries.
Innovation Solution
A system that optimizes a global measure of internal document coherence by identifying and grouping text chunks based on typography, orthography, and page layout features, while removing boilerplate, to construct a hierarchical structure using a polynomial-time algorithm that maximizes coherence and minimizes noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods are used to extract structure from documents, then the process is simple, but the accuracy is low due to inconsistent templates, conversion errors, and boilerplate text
Solution Approach 1:
The patent segments the document structure extraction process into multiple distinct components: physical structure extraction (grouping pixels into sections), logical structure extraction (identifying relationships between sections), and semantic structure extraction (identifying domain-specific section types). This segmentation allows each component to be optimized independently, improving overall accuracy while managing complexity through modular design.
Solution Approach 2:
The patent introduces an intermediary representation layer that captures both physical layout information and logical structure information. This intermediary structure serves as a bridge between the raw document image and the final semantic interpretation, allowing errors in one domain to be compensated by information from another domain, thereby improving robustness against conversion errors and inconsistent templates.
2Reliability
If sequential processing is used for structure extraction, then the system is simple, but errors accumulate in downstream processes
Solution Approach 1:
The patent performs preliminary extraction of multiple structure types (physical, logical, and semantic) in parallel before final integration. By capturing various aspects of document structure simultaneously rather than sequentially, the system prevents error accumulation because each extraction process operates on the original input data independently, and their results are combined at the end rather than passed along a sequential chain.
3Measurement precision
If boilerplate text is not removed, then the processing is faster, but the structure extraction is obscured and less accurate
Solution Approach 1:
The patent applies partial action by selectively removing only the boilerplate text portions from the document while preserving the actual content sections. The system identifies boilerplate based on specific characteristics (repetitive patterns, positioning, formatting) and removes only those elements that interfere with structure extraction, rather than removing all non-content text. This partial removal strikes a balance between improving header identification accuracy and maintaining processing efficiency.
Data Source
AI summary
The present disclosure is directed towards systems and methods for extracting structure and headers from a body of text. This computational extraction is based on the visual and logical similarities between portions of text. Boilerplate is removed from chunks of text making up potential headers and the cleaned result is compared against other potential headers and the remainder of the body of text.