Dynamic KVP Extractor Selection for Document Data Extraction
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Solution Overview
Problem
Automated document processing systems face challenges in handling documents with varying layouts, as different key value pair (KVP) extractors have different levels of efficacy, leading to inconsistent data extraction and potential errors.
Innovation Solution
A method is implemented to obtain and score KVPs from multiple extractors, with a model retraining process initiated when the score falls below a threshold, generating a new set of KVP extractors to enhance extraction efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple KVP extractors are used to handle varying document layouts, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system dynamically selects and switches between multiple KVP extractors based on real-time performance scoring. The extractor with the highest score for a given document layout is automatically chosen, making the system adaptable without requiring manual configuration. This dynamic selection mechanism resolves the contradiction by enabling versatility through multiple extractors while managing complexity through automated performance-based routing.
Solution Approach 2:
The system changes the operational parameters of extractors by computing effectiveness scores based on various metrics (extraction accuracy, processing speed, resource consumption). These parameter changes enable the system to identify and utilize the most effective extractor for each document type, achieving adaptability while keeping the selection logic manageable through quantitative evaluation.
2Manufacturing precision
If model retraining is performed to improve extraction accuracy, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
The system implements continuous feedback by monitoring the performance scores of KVP extractors in real-time. When performance degrades below acceptable thresholds, the system automatically triggers model retraining. This feedback mechanism ensures high extraction accuracy is maintained without requiring continuous retraining, as models are only retrained when necessary, thus reducing time loss while preserving precision.
Solution Approach 2:
Model retraining is performed periodically based on performance degradation rather than continuously. The system monitors extractor effectiveness and initiates retraining only when scores fall below thresholds, creating a periodic rather than continuous retraining cycle. This approach maintains extraction accuracy while minimizing the time lost to retraining operations.
3Reliability
If continuous monitoring and retraining are implemented, then reliability is improved, but use of energy increases
Solution Approach 1:
The system performs self-service by automatically monitoring its own performance and triggering retraining only when necessary. The performance monitoring and threshold-based decision-making enable the system to maintain reliability autonomously without requiring constant external intervention or continuous high-energy operations, thus balancing reliability with energy efficiency.
Solution Approach 2:
Continuous performance monitoring with feedback mechanisms ensures reliability by detecting degradation and triggering corrective retraining. However, the feedback-driven approach means energy-intensive retraining operations are performed only when performance thresholds are breached, not continuously, thereby maintaining reliability while controlling energy consumption through condition-based activation.
Data Source
AI summary
Disclosed embodiments provide techniques for monitoring and evaluating the effectiveness of key value pairs (KVPs) used in a document processing system. In embodiments, KVPs are obtained from multiple extractors of a document processing system. A score is computed for the KVPs by computing an effectiveness metric for each KVP from the multiple KVPs. In response to the computed score being below a predetermined threshold, a model retraining process is performed to generate a new set of KVP extractors, and provide the new set of KVPs to the document processing system.