Paragraph Style Detection for Fixed-to-Flow Document Conversion
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Solution Overview
Problem
Existing document converters struggle to convert fixed format documents into flow format documents, especially when complex elements like mathematical formulas are involved, often prioritizing visual fidelity over flowability, resulting in limited output that requires substantial manual reconstruction.
Innovation Solution
A system that analyzes paragraphs in fixed format documents to classify and group them based on style properties, mapping these properties to a flow format document, allowing for the reconstruction of dominant styles and properties, such as headings or normal styles, to ensure edited documents maintain consistent formatting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing document converters use base techniques to preserve visual fidelity of fixed format documents, then layout consistency is improved, but flowability of the output document deteriorates
Solution Approach 1:
The patent segments the document conversion process into distinct phases: parsing fixed format documents into physical layout elements, analyzing style properties of paragraphs, grouping paragraphs into style clusters based on dominant properties, and mapping to flow format structures. This segmentation allows independent optimization of layout preservation and flowability without compromising either aspect.
Solution Approach 2:
The patent performs preliminary analysis of paragraph style properties (font, spacing, alignment, indentation) before converting to flow format. By pre-identifying dominant style properties and grouping paragraphs into style clusters in advance, the system prepares structured data that enables both visual fidelity preservation and improved flowability during the actual conversion process.
2Manufacturing precision
If existing document converters prioritize visual fidelity preservation, then layout accuracy is improved, but the need for manual reconstruction increases
Solution Approach 1:
The patent implements self-service automation where the system automatically analyzes paragraph style properties, determines dominant properties, groups paragraphs into style clusters, and maps them to flow format documents with appropriate formatting. This automated style reconstruction eliminates the need for manual formatting work while preserving layout accuracy, allowing the system to serve itself rather than requiring user intervention.
Solution Approach 2:
The patent changes the approach from direct pixel-level copying to parameter-based style analysis. By extracting and analyzing style parameters (font family, size, weight, spacing, alignment) and reconstructing them as dominant properties in style clusters, the system achieves layout accuracy through parameter preservation while enabling automated flow format conversion that reduces manual reconstruction needs.
3Manufacturing precision
If complex elements like mathematical formulas are converted using base techniques, then visual fidelity is improved, but the flowability and editability of the output deteriorates
Solution Approach 1:
The patent introduces style clusters as an intermediary data structure between fixed format parsing and flow format output. Style clusters capture dominant style properties of paragraph groups and serve as a mediator that preserves visual fidelity information while enabling flexible flow format representation. This intermediary layer allows complex elements to be represented with both visual accuracy and editability.
Solution Approach 2:
The patent transforms complex element representation from fixed positional data to parameter-based style descriptions. By analyzing and preserving style parameters (font characteristics, spacing, alignment) as dominant properties in style clusters, the system maintains visual fidelity of complex elements like mathematical formulas while converting them to editable flow format structures that retain their formatting through parameter inheritance.
Data Source
AI summary
Embodiments of the present disclosure provide for analyzing paragraphs in a fixed format document to determine style clusters or groupings of each paragraph. In certain embodiments, the paragraphs are grouped into style clusters based on a first property. Each style cluster is then further divided into sub-groups based on a second property. Once the sub-groups have been determined, a third property associated with each paragraph in each sub-group is normalized based on a dominant one of the at least the third property.