Multi-Stage Document Classification for Faster OCR Extraction
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Solution Overview
Problem
Classifying scanned or captured images of physical paper documents is challenging due to variations in document formats, especially for multi-page documents with similar pages, leading to inefficiencies and inaccuracies in optical character recognition and keyword extraction systems.
Innovation Solution
A multi-stage machine learning-based classification process using multiple classifiers, including neural networks, elastic search models, and gradient boosting, to accurately identify and classify digital documents and pages by employing a plurality of classifiers and image analysis techniques, reducing the need for full document scanning and minimizing processor and memory utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If optical character recognition and keyword extraction are used for document classification, then text can be recognized and processed, but processing speed is slow and system efficiency is poor
Solution Approach 1:
The patent segments the document processing task into multiple stages: first extracting structural features (layout, formatting, visual patterns) from document images, then using these features to train specialized classifiers for different document types. This segmentation allows the system to process documents more efficiently by focusing on distinctive structural characteristics rather than analyzing entire document texts, thereby improving both processing speed and classification accuracy.
2Reliability
If extensive processing and memory resources are allocated to document classification, then classification can be performed, but system resource utilization becomes inefficient
Solution Approach 1:
The patent applies local quality by training different specialized classifiers for different document types based on their specific structural characteristics. Each classifier is optimized for particular document formats (e.g., invoices, receipts, forms) and focuses only on the structural features relevant to that document type. This approach improves classification reliability for each specific document type while reducing overall system resource requirements, as resources are allocated efficiently according to the specific needs of each document category rather than using a uniform high-resource approach for all documents.
3Adaptability or versatility
If similar keywords are used for document classification, then keyword matching can be performed, but classification accuracy decreases due to unrelated documents being misclassified
Solution Approach 1:
The patent employs asymmetry by emphasizing structural feature extraction over keyword analysis. Instead of relying on symmetric keyword matching that treats all text equally, the system identifies and weights asymmetric structural characteristics specific to each document type (such as form layouts, field positions, visual patterns). This asymmetric approach to feature extraction enables the system to distinguish between documents with similar keywords by focusing on their unique structural asymmetries, thereby improving classification precision while maintaining adaptability to various document formats.
Data Source
AI summary
In some aspects, the disclosure is directed to methods and systems for machine learning-based data extraction using multiple string searching models. String extraction logic may differ depending on the type of document received. For documents identified to contain line item structures, broader searching models are applied to the document to account for the increased variability of data in the document inherent in data organized in line item structures. For documents identifier to contain non-line item structures, stricter searching models are applied to the document to account for predictable data in the document associated with data organized in non-line item structures.