Machine Learning Key-Value Extraction Across Varied Documents
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Solution Overview
Problem
Existing data extraction techniques for documents such as invoices, purchase orders, and contracts are labor-intensive, error-prone, and inefficient, particularly when dealing with varied document types and layouts, leading to high processing times and costs.
Innovation Solution
A machine learning-based approach using optical character recognition (OCR) and feature engineering to extract key-value pairs from documents, employing a classification model trained with n-gram features and a two-step extraction process to improve accuracy and flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual data extraction methods are used, then flexibility and adaptability to different document types are maintained, but labor intensity and processing time increase significantly
Solution Approach 1:
The patent replaces manual mechanical data extraction with an automated machine learning system that uses optical character recognition (OCR) and classification models to extract key-value pairs from documents, eliminating the need for manual processing while maintaining adaptability to different document types
Solution Approach 2:
The system changes the approach from manual extraction to automated ML-based extraction by transforming documents into structured data through OCR and feature engineering, enabling high-speed processing while maintaining accuracy and flexibility across diverse document formats
2Productivity
If rule-based extraction techniques are used, then processing speed improves, but accuracy and adaptability to varied document layouts deteriorate
Solution Approach 1:
The patent replaces rigid rule-based extraction with flexible machine learning classification models that can adapt to varied document layouts and patterns, achieving both high processing speed and accurate extraction by training models on diverse document examples rather than relying on fixed rules
Solution Approach 2:
The system transitions from static rule-based processing to dynamic ML-based processing where the extraction accuracy is determined by the trained classification model's ability to recognize patterns, enabling high-speed accurate extraction across different document types through feature engineering and model training
3Loss of time
If existing extraction systems are used, then processing time is reduced, but labor costs and error rates increase
Solution Approach 1:
The patent substitutes manual and error-prone extraction processes with an automated machine learning system that consistently applies trained classification models to extract data, reducing human error while maintaining fast processing speeds and eliminating the need for manual verification
Solution Approach 2:
The system incorporates feedback mechanisms through training data that continuously improves the classification models, allowing the system to learn from past extractions and refine its accuracy over time, thereby reducing error rates while maintaining high processing speed
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method achieves high extraction accuracy (above 94.5%) and flexibility, allowing easy extension to other document types and languages, while reducing labor costs and processing times.
Implementation Method 1
applying optical character recognition to a document image, wherein applying optical character recognition outputs a plurality of document objects
Data Source
AI summary
A machine learning based key-value extraction model extracts fields/entities from documents. The input images are processed through OCR. A list of words (uni-grams) and their coordinates are extracted from the original images. Following word cleaning and manipulation, n-gram creation (multi-words), and feature engineering, the transformed data is fed into a classification algorithm to predict if a uni-gram or n-gram is one of the target entities or a non-entity. Following the first step that includes unique feature engineering, a second step improves extraction accuracy among the fields/entities.