Document Template Generation via Text Block Clustering
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Solution Overview
Problem
Current document processing methods require significant human intervention and resources, as they involve tedious identification and classification of various document types, often leading to errors and inefficiencies, especially in high-volume environments like insurance claims processing.
Innovation Solution
A network-based template generation system that dynamically identifies and generates templates from a batch of documents by analyzing text blocks based on their spatial location and value, clustering matching text blocks, and comparing document arrays to determine matching document types without prior training, thereby streamlining the processing of mixed document types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human personnel manually identify and review documents, then document classification accuracy can be maintained, but processing time and labor costs increase significantly
Solution Approach 1:
The system enables documents to self-classify by automatically extracting text blocks, generating frameworks, and comparing documents against each other without human intervention. The automated template generation and matching process allows the system to serve itself in identifying and classifying document types, eliminating the need for manual review while maintaining accuracy through algorithmic comparison.
2Extent of automation
If existing automated methods train models using datasets, then document processing can be automated, but significant computing resources and training time are required
Solution Approach 1:
The system performs preliminary actions by extracting text blocks and generating frameworks from documents before any classification or matching occurs. This preliminary structuring of data into comparable frameworks enables subsequent automated processing without requiring extensive training, as the framework generation creates a standardized format that can be directly compared across documents.
Solution Approach 2:
The system creates simplified copies of document structures in the form of frameworks that capture essential text blocks and their relationships. These framework copies are much lighter than full document training datasets, requiring significantly fewer computing resources to process while retaining the essential information needed for classification and matching.
3Reliability
If manual document identification is performed, then document types can be properly categorized, but human error and inconsistency increase
Solution Approach 1:
The system segments documents into discrete text blocks with defined spatial locations and values, creating standardized units that can be consistently processed. By dividing documents into comparable framework elements, the system eliminates the variability inherent in manual identification while maintaining reliability through systematic, rule-based comparison of segmented components.
Data Source
AI summary
A template generation system for generating document templates from a mixed set of document types including a template generation server programmed to receive a batch of documents, identify a plurality of text blocks, generate a plurality of clusters, generate a plurality of document arrays corresponding to the plurality of clusters, and compare each document array to each other document array to determine a percentage match. When the percentage match between two or more frameworks exceeds a threshold, the template generation system defines a subset of documents, and for each subset of documents, template generation system generates a template for the subset of documents. The template is a collection of the text blocks that are commonly included in each of the documents of the subset.