Document Data Extraction Using Spatial Feature Clustering
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Solution Overview
Problem
Manual data extraction from semi-structured documents is cumbersome, error-prone, and lacks scalability, particularly in segregating documents for financial accounting purposes.
Innovation Solution
A method and system that utilize spatial features, including text, layout, and location features, to determine variance and layout for document clustering and data extraction, employing multiple machine learning models for feature selection and data extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual management and data extraction methods are used for semi-structured documents, then flexibility in handling various document types is maintained, but processing efficiency and accuracy deteriorate
Solution Approach 1:
The patent replaces manual mechanical data extraction processes with an automated system that uses machine learning models and spatial feature analysis. The system automatically extracts data from semi-structured documents by analyzing spatial relationships between text elements, replacing the need for manual handling while maintaining high accuracy through intelligent algorithms.
Solution Approach 2:
The patent transforms the approach to data extraction by changing from rule-based parameters to spatial feature parameters. Instead of relying on predefined rules for each document type, the system analyzes spatial relationships (positions, distances, arrangements) of text elements to automatically determine data extraction strategies, adapting to different document types through parameter transformation rather than manual rule configuration.
2Adaptability or versatility
If rule-based techniques are used to segregate documents, then processing speed is maintained, but versatility and scalability deteriorate
Solution Approach 1:
The patent creates a universal document processing system that can handle multiple document types (invoices, purchase orders, receipts, etc.) through a single spatial feature analysis framework. The same core technology analyzes spatial relationships regardless of document type, making the system versatile without requiring separate rule sets for each document category.
Solution Approach 2:
The patent uses clustering algorithms to create representative layout models from training documents. These clustered layout patterns serve as templates that can be applied to similar documents, allowing the system to generalize from examples rather than requiring explicit rules for each document type, thereby reducing complexity while maintaining versatility.
3Productivity
If manual data extraction is performed for financial documents, then accuracy can be monitored, but processing time and error rates worsen
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously learns from extracted data and adjusts its spatial feature analysis accordingly. The machine learning models are trained on labeled data and improve their accuracy over time through feedback loops, ensuring reliable financial data extraction while maintaining high processing speeds through automated operations.
Solution Approach 2:
The patent performs preliminary clustering and layout analysis on training documents before actual data extraction. By pre-processing and organizing spatial features in advance, the system prepares optimized extraction strategies that can be quickly applied to new documents, ensuring both speed and accuracy in financial data processing.
Data Source
AI summary
A method and system of extracting data from a set of documents is disclosed. A processor determines a plurality of spatial features for each of the set of documents based on a set of keywords and a set of entities extracted from the documents. A variance is determined between at least one of the plurality of spatial features determined for each of the documents. Layout is determined based on the spatial features and variance. Each set of documents is clustered in at least one cluster based on a similarity between the layouts of the documents using a first machine learning model. Spatial features are selected using a second machine learning model. Data of the set of entities is extracted corresponding to the keywords in the documents based on the selection of the features and the similarity between the layouts using a third machine learning model.