Image Reading Entity Extraction With Ensemble Validation
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Solution Overview
Problem
Current image reading systems struggle with converting unstructured form-like documents into structured digital formats, requiring manual intervention and suffering from inaccuracies due to unstructured data, spelling errors, and limitations in entity recognition and grouping, especially in financial documents like loan notices.
Innovation Solution
An image reading system that uses a machine learning kernel to generate bounding boxes and search paths for textual blocks, combined with a descriptive linguistics engine to identify target textual block pairs, enabling automated entity extraction and validation without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional OCR mechanisms are used to process unstructured form-like documents, then the system can recognize textual content, but the extraction accuracy deteriorates due to lack of structured data and unstructured information
Solution Approach 1:
The system segments the unstructured document into multiple textual blocks using a machine learning kernel, then processes each block independently to extract entities. This segmentation transforms the unstructured data into manageable units that can be systematically processed and validated.
Solution Approach 2:
The patent introduces an intermediary validation process that uses a database of known entities and relationships to verify extracted information. This intermediary layer filters and validates the extracted entities, improving accuracy by cross-referencing with trusted data sources.
2Measurement precision
If manual data extraction is performed on unstructured documents, then extraction accuracy can be maintained through human judgment, but the time consumption increases significantly
Solution Approach 1:
The system performs self-service through automated entity extraction using machine learning models that process documents independently without human intervention. The automated validation process further enhances this self-service capability by automatically verifying extracted entities against known databases, eliminating the need for manual verification while maintaining high accuracy.
3Adaptability or versatility
If unstructured data is processed without predefined data models, then the system can handle diverse document formats, but the entity recognition and grouping capabilities deteriorate
Solution Approach 1:
The system employs a universal machine learning kernel that can process multiple document formats and types without requiring predefined data models for each specific format. This universal approach maintains adaptability across diverse documents while using a common extraction and validation framework to ensure consistent entity recognition accuracy.
4Productivity
If OCR processes text to generate digital text, then the information can be archived and searched, but spelling and typographical errors are introduced that affect machine learning model performance
Solution Approach 1:
The validation process incorporates feedback mechanisms where extracted entities are cross-checked against known databases and relationships. This feedback loop identifies and corrects spelling and typographical errors introduced during OCR processing, ensuring that the final extracted information is accurate and reliable for machine learning models.
Data Source
AI summary
Entity extraction is performed using a voting ensemble approach to enhance the accuracy and effectiveness of entity extraction processes. Voting ensemble weights are stored. In turn, a prediction value for a first entity is determined from multiple models that are specifically trained for entity extraction. A plurality of first entity prediction values is obtained. The model with the highest prediction value among the plurality of first entity prediction values is then selected.