Document Data Extraction Using Region and Label Tensors
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Solution Overview
Problem
Legacy databases containing valuable data face obsolescence, rendering their data largely useless beyond record preservation, and existing methods are inefficient in extracting relevant information from both legacy and current databases.
Innovation Solution
A system and method that utilizes a controller to create region and label tensors from legacy database documents, training an extraction algorithm to identify and extract target data from additional documents, thereby enhancing data extraction efficiency and reducing memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data extraction methods are used on legacy databases, then data can be preserved, but processing speeds are slow and memory usage is high
Solution Approach 1:
The system performs preliminary action by training the extraction algorithm on a subset of legacy database documents before actual extraction. This pre-training phase prepares the model to efficiently process remaining documents, improving subsequent extraction speed while reducing memory requirements during the actual extraction process.
Solution Approach 2:
The legacy database documents are segmented into training subsets and extraction targets. The system divides the large document set into manageable portions for training purposes, allowing the algorithm to learn from representative samples without loading all documents into memory simultaneously, thus reducing memory usage while maintaining extraction effectiveness.
2Loss of information
If legacy database data is preserved without processing, then record integrity is maintained, but data value is limited
Solution Approach 1:
The system extracts valuable information from legacy database documents by training an extraction algorithm to identify and pull specific target data elements. This extraction process transforms preserved records into actionable, high-value data without losing the original record integrity, thereby increasing data value while managing processing complexity through targeted extraction rather than full document processing.
Solution Approach 2:
The extraction algorithm serves as an intermediary between the legacy database and modern data systems. It mediates the transformation of old-format documents into structured, usable data formats, enabling legacy data to provide value in contemporary applications without requiring direct manipulation of the original complex document structures.
3Measurement precision
If extraction algorithms are trained on all legacy documents, then extraction accuracy improves, but processing time increases
Solution Approach 1:
The system applies partial action by training the extraction algorithm on a carefully selected subset of legacy documents rather than processing all documents. This subset is sufficient to capture the diversity and characteristics needed for accurate extraction, achieving high extraction accuracy while significantly reducing training time compared to using the complete document set.
Solution Approach 2:
The system changes parameters by adjusting the training dataset size and composition to optimize the balance between accuracy and training time. By modifying parameters such as the proportion of documents used for training and the diversity of document types included, the system achieves satisfactory extraction accuracy with reduced processing time.
Data Source
AI summary
Systems and methods for enabling target data to be extracted from documents are disclosed herein. In an embodiment, a method of enabling target data to be extracted from documents includes accessing a database including a plurality of documents including target data, for each of multiple of the documents, creating a region tensor based on extracted text including the target data, for each of the multiple of the documents, creating a label tensor based on an area including the target data, and using the region tensor and the label tensor, training an extraction algorithm to extract the target data from additional documents.