Dynamic OCR Data Extraction with Hierarchical Profiles
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Solution Overview
Problem
Current OCR systems face challenges in efficiently processing documents with different formatting and data types, requiring presorting which is burdensome for users, and lack a mechanism to dynamically adjust extraction rules based on document content types.
Innovation Solution
The system employs dynamic OCR-based data extraction with data element profiles and overrides, allowing modification of rules at various levels within the content category, document type, or form type, using hierarchical data models to identify and extract data elements like sections, tables, and fields, and applying specific extraction instructions based on detected content types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If presorting is implemented to handle different document formats, then data extraction accuracy is improved, but user burden and system complexity increase
Solution Approach 1:
The system performs automatic document classification and rule selection without requiring user intervention. The OCR system autonomously identifies document types and applies appropriate extraction rules, eliminating the need for users to manually presort documents while maintaining high extraction accuracy
Solution Approach 2:
The system dynamically adjusts extraction parameters and rules based on detected document characteristics. By changing extraction parameters according to document type automatically, the system achieves high accuracy without requiring users to manually configure settings for different document formats
2Device complexity
If fixed extraction rules are used for all document types, then system complexity is reduced, but adaptability to different document formats deteriorates
Solution Approach 1:
The system employs dynamic rule selection where extraction rules are automatically adjusted based on detected document types. The system transitions from static fixed rules to dynamic adaptive rules that change according to document characteristics, maintaining simplicity while improving adaptability
Solution Approach 2:
The system uses a universal classification framework that handles multiple document types through a single integrated system. By implementing multi-functionality at the classification level, the system can adapt to various document formats without requiring separate complex extraction systems for each type
3Measurement precision
If manual presorting is required for different content types, then extraction precision is improved, but processing time increases
Solution Approach 1:
The system performs automatic document classification as a preliminary step before extraction, identifying document types and selecting appropriate rules in advance. This automated preliminary action eliminates manual presorting time while ensuring precision by preparing the correct extraction configuration before processing begins
Solution Approach 2:
The system uses feedback from document analysis to automatically adjust extraction parameters. By continuously monitoring document characteristics and adjusting rules accordingly, the system achieves high precision extraction without manual intervention, reducing processing time while maintaining accuracy
Data Source
AI summary
A method for dynamic optical character recognition based data extraction includes: analyzing an image; detecting a first identifier associated with a first content type in an image; providing a first data extraction model for the first content type, the first data extraction model including definitions for a plurality of data types; performing an optical character recognition pass on the image to identify a plurality of characters of the image; and extracting a set of data elements from the image based on the first data extraction model and the plurality of characters of the image identified by performing the optical character recognition pass on the image.