Label-Value Data Extraction from Tabulated Documents
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Solution Overview
Problem
Existing methods for extracting structured data from electronic documents, especially those with tabulated layouts like forms and invoices, face challenges in accurately identifying and separating label:value pairs due to variations in layout and lack of correspondence between data fields and their layout structures, leading to inefficiencies and reduced confidence in data extraction.
Innovation Solution
A method and system that perform layout analysis to generate structural elements, identify and tag textual elements as label and value pairs, and extract these pairs from digital documents using a sequence-based algorithm, enabling robust extraction of label:value data even in complex layouts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If geometric page layout analysis algorithms are used to segment document pages into text blocks and image blocks, then the document structure can be recognized and elements can be delimited, but the methods mainly aim to delimit boxes one dimensionally into columns and do not effectively handle tabulated layouts with label:value pairs
Solution Approach 1:
The patent segments the document layout into hierarchical structures (pages → regions → blocks → lines → elements) and further segments label:value pairs into distinct components. This multi-level segmentation enables the system to identify and extract label:value pairs from complex tabulated layouts by breaking down the document structure into manageable units that can be processed sequentially.
Solution Approach 2:
The patent transitions from traditional one-dimensional column-based segmentation to two-dimensional tabular structure analysis. By introducing row and column concepts and analyzing the spatial relationships between elements in both horizontal and vertical dimensions, the system can effectively identify label:value pairs in tabulated layouts that previous methods could not handle.
2Productivity
If traditional layout analysis methods are used to extract data from documents, then text regions and image regions can be identified, but there is a lack of correspondence between data fields and their layout structures leading to reduced confidence in extraction
Solution Approach 1:
The patent implements feedback mechanisms where the system analyzes the extracted label:value pairs and uses this information to refine its understanding of the document structure. The method incorporates confidence scoring and validation steps that provide feedback on extraction quality, allowing the system to adjust its analysis approach and improve precision while maintaining productivity.
Solution Approach 2:
The patent replaces traditional mechanical layout analysis methods with a more sophisticated system that combines geometric analysis, typographical feature recognition, and content-based classification. This substitution enables the system to establish correspondence between data fields and layout structures by analyzing multiple features simultaneously, thereby improving measurement precision without sacrificing extraction productivity.
3Adaptability or versatility
If multiple templates are used to handle variations in document layouts, then different layout types can be accommodated, but the complexity of the extraction system increases and multiple templates are needed
Solution Approach 1:
The patent creates a universal extraction framework that can handle multiple document layout types without requiring separate templates for each format. The system uses generic layout analysis algorithms that adapt to different document structures by analyzing the actual geometric and typographical features present in each document, enabling one system to perform multiple extraction functions across varied layouts.
Solution Approach 2:
The patent implements a dynamic extraction system that adapts its analysis approach based on the characteristics of each document. Rather than using static templates, the system dynamically adjusts its segmentation and identification strategies by analyzing the actual layout patterns, element relationships, and typographical features present in the specific document being processed, thereby reducing system complexity while maintaining versatility.
Data Source
AI summary
This disclosure provides an exemplary method and system for extracting structured label and value pairwise textual data from a textual document. According to an exemplary method, initially a layout analysis is performed resulting in one or more alternatives for grouping and ordering the textual elements of interest. Next, textual elements are tagged as including a label term, a value term or a label and value term. Finally, a sequence-based method is applied to the tagged elements to generate one or more sequence listings representative of the label and value pairwise data structure(s) and label:value pairwise data is extracted.