Document Layout Clique Discovery for Adaptive Extraction
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Solution Overview
Problem
Existing methods for extracting information from unstructured documents face challenges due to high layout variability, requiring either time-consuming visual inspection or inefficient processing, especially when dealing with documents that have both static and dynamic layouts.
Innovation Solution
An adaptive method that automatically determines the appropriate content extraction approach for each document by using clique discovery to group similar layouts and deciding on a field-by-field basis whether to apply a positional method or a machine learning approach, allowing for continuous learning and handling new template-based representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a positional method is used for template-based documents, then extraction speed and cost are reduced, but precision deteriorates when key or value fields are not at expected locations
Solution Approach 1:
The system dynamically adapts the extraction method based on document characteristics. It evaluates whether a document is template-based or unstructured and automatically selects between positional methods and machine learning models, making the extraction approach flexible rather than static.
Solution Approach 2:
The system changes the extraction parameters (method selection) based on document properties. By analyzing document features and comparing them against known templates, the system adjusts which extraction methodology to apply, optimizing both speed and precision for different document types.
2Adaptability or versatility
If a machine learning model is applied to handle dynamic layouts, then adaptability to layout variability is improved, but processing time and cost increase
Solution Approach 1:
The system segments the document processing task by dividing documents into template-based and unstructured categories. Only unstructured documents with high layout variability are processed using machine learning models, while template-based documents use faster positional methods, reducing overall processing time.
Solution Approach 2:
The system applies machine learning models only partially - specifically to documents where they are truly needed (unstructured documents with high variability). For the majority of template-based documents, simpler and faster methods are used, avoiding excessive application of complex models.
3Measurement precision
If visual inspection is performed to determine processing approach, then accuracy of method selection is improved, but processing efficiency deteriorates
Solution Approach 1:
The system replaces manual visual inspection with an automated evaluation mechanism. The system automatically analyzes document features, compares them against stored templates, and determines the appropriate extraction method without human intervention, maintaining accuracy while dramatically improving efficiency.
Solution Approach 2:
The system performs self-evaluation by automatically assessing its own capability to handle different document types. It uses built-in template matching and document analysis features to determine whether positional methods or machine learning models are appropriate, eliminating the need for external visual inspection.
4Reliability
If both positional method and machine learning model are applied on a field-by-field basis, then extraction completeness is improved, but system complexity and processing time increase
Solution Approach 1:
The system dynamically selects a single extraction method based on overall document characteristics rather than mixing methods field-by-field. This reduces system complexity by having one coherent extraction strategy per document while maintaining completeness through accurate method selection.
Data Source
AI summary
One example method includes, for each document in a group of annotated documents, extracting a set of words from the annotated document, and each of the words is positioned in a respective field of the annotated document. The method further includes using an aggregation function to determine, for one of the fields, a similarity of each one of the annotated documents to all of the other annotated documents, creating a document layout graph with nodes that each correspond to a respective annotated document, and each node is connected to all other nodes for which a similarity threshold for the one field has been met, and running an algorithm on the document layout graph to identify a clique of the annotated documents, and each annotated document in the clique has a similar layout to respective layouts of the other annotated documents in the clique.