Document Structural Block Extraction via Dynamic Rule Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing document processing techniques struggle to accurately extract structural blocks from documents due to the lack of standardized tags or properties in formats like PDF, and are limited in their applicability across different domains, especially when dealing with varying document layouts and structures.
Innovation Solution
A method and system that extract text lines from documents, generate feature vectors, determine dominant features and factors for structural classes, and derive rules for classification, allowing for the automatic tagging of structural blocks such as paragraphs, titles, and footnotes, without requiring pre-defined templates or domain-specific knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If template-based approaches are used for extracting logical structures, then classification accuracy is improved for documents with consistent layouts, but adaptability deteriorates when dealing with documents having large variations in layout
Solution Approach 1:
The system dynamically adapts its classification rules based on the actual document layout observed during processing. Instead of using fixed templates, the system learns and adjusts classification criteria in real-time according to the specific document's structural characteristics, enabling it to handle both consistent and varied layouts effectively
Solution Approach 2:
The system changes its operational parameters (classification rules) based on the input document's features. By analyzing the actual layout parameters of each document and adjusting the classification thresholds and criteria accordingly, the system maintains high accuracy across diverse document types without requiring pre-defined templates for each layout variation
2Productivity
If pre-defined templates are used for document structure extraction, then processing speed is improved for matching document types, but device complexity increases due to template specification and maintenance
Solution Approach 1:
The system performs self-learning by automatically analyzing document structures and generating its own classification rules without requiring external template specification. This eliminates the complexity of template creation and maintenance while maintaining fast processing speeds, as the system adapts autonomously to different document types through repeated exposure and learning
3Measurement precision
If domain-specific techniques are used for structure extraction, then extraction accuracy is improved for specific document types, but adaptability deteriorates across different domains
Solution Approach 1:
The system is designed with universal classification capabilities that can handle multiple document types and domains through a single unified framework. By learning general structural patterns that apply across domains while adapting to domain-specific characteristics through the dynamic rule adjustment mechanism, the system achieves both high extraction accuracy and broad cross-domain applicability without requiring separate domain-specific techniques
Data Source
AI summary
This disclosure relates to method and system for determining structural blocks of a document. The method may include extracting text lines from the document, generating a feature vector for each text line by determining feature values for a set of features in the each text line, and determining at least one dominant feature from among the set of features and at least one corresponding dominance factor, for each structural class, based on the feature vector for each text line. The method may further include deriving a set of rules for classification of the text lines into respective structural classes and determining a structural block tag for each text line based on the set of rules. Each of the set of rules correspond to one of the structural classes and is based on the at least one dominant feature and the at least one corresponding dominance factor for that class.