Adaptive Document Understanding via Feature Space Clustering
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Solution Overview
Problem
Current Smart Document Understanding (SDU) systems face challenges in converting unstructured documents to structured data due to varying page formats, leading to decreased conversion accuracy, as they often rely on minimal and biased training data extraction methods.
Innovation Solution
The approach involves creating multiple clusters in feature space from unstructured pages, assigning machine learning models to each cluster based on their similarity, and adaptively selecting the best-fit model for transformation, enhancing data conversion accuracy by handling different page formats effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single machine learning model is used to cover various page formats, then device complexity is reduced, but conversion accuracy decreases
Solution Approach 1:
The patent divides the single model approach into multiple specialized machine learning models, each trained on specific page format clusters. This segmentation allows each model to specialize in particular formats (e.g., 2-column layouts, graphical images, paragraph-based letters) rather than attempting to handle all formats generically, thereby improving conversion accuracy while managing complexity through organized specialization.
Solution Approach 2:
The patent implements local quality by assigning different model characteristics to different page format clusters. Each cluster receives a model optimized for its specific format features, creating local expertise rather than uniform handling. This ensures that each page format receives the appropriate level of specialized attention matched to its unique characteristics.
2Ease of operation
If random sampling is used for training data extraction, then ease of operation is improved, but training data quality deteriorates due to format bias
Solution Approach 1:
The patent applies preliminary action by performing clustering analysis on training documents before the actual model training process. This preliminary organization of training data into format-based clusters ensures that subsequent sampling and model training are based on well-structured, representative groups rather than random or biased selections, improving training data quality while maintaining operational efficiency.
Solution Approach 2:
The patent introduces clustering as an intermediary step between raw training data and model training. This intermediary process organizes unstructured training documents into meaningful clusters based on page format characteristics, serving as a bridge that transforms random sampling into representative sampling without requiring complex manual curation.
3Measurement precision
If multiple machine learning models are used for different page formats, then conversion accuracy is improved, but device complexity increases
Solution Approach 1:
The patent implements universality by creating a multi-functional system that can handle various page formats through a unified clustering and model selection framework. The system universally processes different document types (2-column formats, graphical images, paragraph-based letters) by routing them to appropriate specialized models, achieving multi-format capability through an organized architecture rather than ad-hoc complexity.
Solution Approach 2:
The patent uses clustering as an intermediary mechanism that manages the complexity of multiple models. Instead of directly managing many specialized models, the system uses format-based clustering to automatically route documents to appropriate models, serving as an intermediary layer that simplifies model management while maintaining high conversion accuracy through specialized processing.
Data Source
AI summary
An approach is provided in which a method, system, and program create a plurality of page clusters in feature space from a plurality of feature vectors corresponding to a plurality of unstructured pages. The method, system, and program product assign one of a plurality of machine learning models to each one of the plurality of page clusters based on a relationship in the feature space between the plurality of page clusters and a plurality of training clusters corresponding to the plurality of machine learning models. The method, system, and program product identify one of the plurality of page clusters that corresponds to a selected one of the plurality of unstructured pages, and transform the selected unstructured page into a structured page using a selected one of the plurality of machine learning models assigned to the identified page cluster.