Document Provider Embeddings for Accurate OCR-Based Association
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing document processing systems struggle to accurately associate documents with their respective providers due to non-uniform formats, leading to inefficiencies and inaccuracies in automated document management.
Innovation Solution
A machine learning-based approach that utilizes two models to generate embeddings for document providers within a document provider space, using optical character recognition and pre-training with labeled data to determine document-provider associations efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional techniques such as term frequency-inverse document frequency (TD-IDF) or fuzzy matching are used to associate documents with document providers, then the system can process documents automatically, but the accuracy of association is insufficient
Solution Approach 1:
The patent transforms the document association problem from traditional text matching parameters to embedding space parameters. Documents and document providers are represented as vectors in a high-dimensional space, and association is determined by computing similarity metrics (e.g., cosine similarity) between these embeddings. This parameter transformation enables both high accuracy through nuanced semantic comparison and efficient automated processing through vector operations.
Solution Approach 2:
The patent replaces traditional mechanical text-matching systems (TD-IDF, fuzzy matching) with a machine learning-based embedding system. Instead of relying on keyword frequency and simple string matching algorithms, the system uses trained neural network models to generate semantic embeddings, substituting the mechanical matching process with intelligent vector-based comparison that achieves superior accuracy while maintaining automation.
2Productivity
If manual document management is implemented for large document receivers, then document-provider association can be accurately tracked, but the process becomes too cumbersome and inefficient
Solution Approach 1:
The patent implements a self-service automated system where the document processing system independently performs entity recognition, embedding generation, and provider association without human intervention. The machine learning models automatically extract document provider information from incoming documents, compute embeddings, and determine associations through similarity comparison, enabling large-scale document processing with both high efficiency and operational simplicity.
3Measurement precision
If machine learning models are trained with labeled data from multiple sources, then the accuracy of document-provider association is improved, but the computational resources and training time increase
Solution Approach 1:
The patent performs preliminary action by pre-training machine learning models on comprehensive labeled datasets from multiple sources before deployment. This upfront training investment creates robust embedding models that can accurately associate documents with providers across diverse formats and domains. The pre-trained models are then reused for ongoing document processing, amortizing the initial training time cost over numerous subsequent inferences and achieving both high accuracy and operational efficiency.
Data Source
AI summary
A document is received from a document provider. A representation of the document provider associated with the document within a document provider space is determined based at least in part on text boxes and corresponding coordinates associated with the text boxes within the document. The document provider associated with the document is determined based on a measure of similarity. A database is updated to associate the document with the determined document provider.