Content Extraction via Reference Object Metadata Matching
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Solution Overview
Problem
Current machine learning models for content extraction from documents, such as deep neural networks, require large labeled training datasets and significant computational resources, and struggle with accuracy and efficiency, especially when dealing with new document types or distorted images, leading to insufficient performance in applications like financial form analysis where small errors can have substantial consequences.
Innovation Solution
The proposed solution involves a methodology that uses a repository of pre-analyzed reference documents to perform structural comparisons with target documents, employing metadata and homography matrices to accurately extract key-value pairs, reducing the need for extensive training data and computational resources by leveraging the layout-aware analysis of frequently encountered document categories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep neural network models are used for content extraction, then the model can handle complex document structures, but the requirement for large labeled training datasets and significant computational resources increases
Solution Approach 1:
The patent uses template copying where pre-designed templates representing document structures are copied and matched against target documents. Instead of training models on large datasets, the system copies and adapts templates to extract content, significantly reducing data requirements while maintaining extraction accuracy for common document types.
Solution Approach 2:
The system performs preliminary actions by pre-designing and storing templates for common document structures before actual content extraction is needed. These templates are prepared in advance with their structural information, allowing rapid matching and extraction without requiring extensive training data at execution time.
2Reliability
If deep learning models are used for content extraction, then the model can process documents, but the computational resources and time required increase
Solution Approach 1:
The patent replaces the mechanical/computational intensive deep learning neural network system with a template-based matching system. This substitution uses simpler pattern recognition and template matching algorithms that consume significantly fewer computational resources while maintaining accuracy for structured document content extraction.
Solution Approach 2:
By copying pre-designed templates instead of performing complex neural network computations, the system reduces computational energy consumption. The template copying approach uses basic string matching, layout comparison, and pattern recognition operations that are much more energy-efficient than training and executing deep learning models.
3Adaptability or versatility
If machine learning models are retrained for new document types, then the model can handle new formats, but the time and computational resources required increase
Solution Approach 1:
The system performs preliminary action by pre-preparing templates for common document types in advance. When new document types are encountered, the system can rapidly adapt by matching against existing templates or creating new templates without requiring time-consuming retraining, thus maintaining adaptability while minimizing time loss.
Solution Approach 2:
The template system is dynamic in that it can adapt to new document types by adding new templates or modifying existing ones, without requiring complete model retraining. This dynamic template addition approach allows the system to adapt to new formats quickly while maintaining performance on previously learned document types.
4Reliability
If deep learning models are used for content extraction, then the model can extract information, but the speed of extraction decreases compared to simpler methods
Solution Approach 1:
The patent substitutes complex neural network processing with simpler template matching mechanics. This replacement uses basic pattern recognition, string matching, and layout comparison operations that execute much faster than deep learning inference, thereby increasing extraction speed while maintaining accuracy through the structured template approach.
Data Source
AI summary
Representations of sets of descriptors of reference objects are stored in a repository, with individual descriptors including information about entities identified in the reference objects. In response to a request to extract content from a particular data object, a reference object which satisfies a similarity criterion with respect to the particular data object is identified from the repository using the descriptors. A structural comparison of the particular data object and the reference object is performed to determine an entity related to another entity identified in the particular data object.