Line Item Detection in Borderless Tabular Data
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Solution Overview
Problem
The analysis of borderless tabular data without visible borders poses challenges in identifying and separating text lines, leading to difficulties in accurate content extraction and semantic understanding due to structural variations and misalignment of key-value pairs.
Innovation Solution
A computer-implemented method identifies and classifies text separators in borderless tables into target clusters based on property information, providing indication information for separator types to facilitate line item detection and improve text separation and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If text separators are not classified into different types, then the analysis process is simpler, but line item detection accuracy deteriorates due to inability to distinguish different separator functions
Solution Approach 1:
The patent segments text separators into different types (horizontal separators, vertical separators, and unknown separators) based on their orientation and function. This segmentation allows the system to distinguish between separators that divide rows versus those that divide columns, thereby improving line item detection accuracy without overwhelming complexity
Solution Approach 2:
The patent changes the parameter of separator identification from simple detection to classification based on multiple parameters including orientation (horizontal/vertical), position relative to text lines, and spacing characteristics. This multi-parameter approach enables accurate distinction between different separator types, resolving the contradiction between accuracy and complexity
2Measurement precision
If borderless tables are analyzed without considering separator types, then processing is faster, but text line separation accuracy deteriorates due to structural variations
Solution Approach 1:
The patent performs preliminary classification of text separators into different types before proceeding with text line separation. By pre-categorizing separators as horizontal, vertical, or unknown based on their properties, the system establishes a structured foundation that accelerates subsequent processing while maintaining high separation accuracy
Solution Approach 2:
The patent applies different analysis strategies to different types of separators locally. Horizontal separators are used to identify row boundaries, vertical separators for column boundaries, and unknown separators are handled with additional verification. This localized approach optimizes processing efficiency for each separator type while maintaining overall accuracy
3Ease of operation
If all text separators are treated uniformly, then the analysis method is simpler, but key-value pair alignment deteriorates due to structural variations in borderless tables
Solution Approach 1:
The patent segments the analysis process into distinct stages: separator identification, separator classification, and key-value pair alignment. By classifying separators into types first, the system creates a reliable structural framework that guides subsequent alignment operations, ensuring that key-value pairs are correctly matched even in complex borderless table structures
Solution Approach 2:
The patent introduces separator type classification as an intermediary step between raw separator detection and key-value pair alignment. This intermediate classification layer provides crucial structural information that mediates the alignment process, enabling reliable matching while maintaining operational simplicity through automated classification rules
Data Source
AI summary
In an approach, a processor identifies a plurality of text separators in a borderless table, a text separator of the plurality of text separators defining a non-text region between two consecutive text lines in the borderless table. A processor classifies the plurality of text separators into a number of target clusters comprised in a target group based on property information related to the plurality of text separators, the number of target clusters corresponding to a number of separator types. A processor provides indication information to indicate respective separator types of the plurality of text separators based on a result of the classifying.