Document Preprocessing for Accurate Type Prediction and Extraction
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Solution Overview
Problem
Traditional document data extraction systems struggle with inconsistent and inaccurate classification of document types due to varying layouts, contents, and configurations, requiring manual document segregation and are prone to human errors, leading to inefficiency and resource wastage.
Innovation Solution
A system utilizing a document preprocessing engine that performs augmentation and transformation of training documents to enhance a document type prediction model, enabling automatic classification and selection of appropriate data extraction services through deep learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual segregation of documents by users is used to classify document types, then document type classification can be performed, but it is time-consuming and prone to human errors
Solution Approach 1:
The patent replaces the manual mechanical classification process with an automated machine learning system. The document type prediction model uses deep learning algorithms to automatically classify documents into different types, eliminating the need for manual user segregation while improving accuracy and reducing time consumption.
Solution Approach 2:
The system enables documents to classify themselves automatically through the prediction model. Each document is processed by the model which autonomously determines its type based on learned patterns from training data, without requiring human intervention or manual categorization efforts.
2Reliability
If traditional data extraction systems are used, then data extraction can be performed, but consistency and accuracy are compromised due to varying layouts, contents, and configurations
Solution Approach 1:
The patent transforms the classification approach by changing parameters from rule-based thresholds to probabilistic predictions. The model outputs prediction scores that indicate confidence levels, allowing the system to handle varying document layouts and configurations consistently by selecting extraction services based on these probabilistic parameters rather than rigid rules.
Solution Approach 2:
The system segments the document processing task into distinct stages: document type prediction, service identification, and data extraction. This segmentation allows each component to specialize in handling specific aspects of document variations, improving overall consistency while managing complexity through modular architecture.
3Manufacturing precision
If multiple data extraction services are maintained for different document types, then accurate extraction can be achieved, but service selection becomes complex without automated prediction
Solution Approach 1:
The document type prediction model serves multiple functions: it classifies documents, predicts appropriate extraction services, and provides confidence scores for decision-making. This multi-functionality simplifies the overall system operation by consolidating classification and service selection tasks into a single predictive model, making the system easier to operate while maintaining high extraction accuracy.
Data Source
AI summary
Techniques described herein relate to a method for performing preprocessing of documents for data extraction. The method includes obtaining a document preprocessing request; in response to obtaining a document preprocessing request: obtaining a document associated with the document preprocessing request; performing data preparation on the document to generate an updated document; generating a document type prediction using the updated document and a document type prediction model; identifying a data extraction service of a plurality of data extraction services associated with the document type prediction; and initiating further processing of the document to perform data extraction using the identified data extraction service.