Client-Side Document Classification Using OCR Before Upload
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Solution Overview
Problem
Users often incorrectly identify or upload incorrect documents during the electronic document upload process, leading to delays and inconvenience in processing.
Innovation Solution
A method and system for real-time document classification on user equipment using optical character recognition (OCR) and machine learning algorithms to identify and classify document sections, allowing for quick and accurate document classification before transmission to an application server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If document classification is performed manually by users, then the system requires minimal processing resources, but the accuracy of document identification deteriorates leading to incorrect uploads
Solution Approach 1:
The document classification system performs automatic document type identification without requiring user intervention. The system extracts text from uploaded documents using OCR, analyzes the extracted text content, and automatically determines the document type, allowing the system to serve itself rather than relying on manual user classification
Solution Approach 2:
The patent replaces the manual mechanical process of user document classification with an automated optical and computational system. OCR technology converts document images to text, and machine learning algorithms automatically classify documents, substituting human manual sorting with automated technological processes
2Reliability
If full document analysis is performed to ensure accurate classification, then the classification accuracy improves, but the processing time increases causing delays
Solution Approach 1:
The system performs partial document analysis by extracting and analyzing only specific text portions that are most indicative of document type, rather than processing the entire document. This selective approach maintains classification accuracy while reducing overall processing time
Solution Approach 2:
The system performs preliminary OCR text extraction and analysis before final document classification is needed. By preparing and analyzing document content in advance, the system can quickly determine document types without delays during the actual upload process
3Productivity
If lightweight algorithms are used for real-time classification, then the processing speed improves, but the measurement precision of document type identification may deteriorate
Solution Approach 1:
The patent segments the document analysis process into distinct stages: OCR text extraction, text preprocessing, key feature identification, and classification. This segmentation allows lightweight algorithms to focus on specific tasks efficiently while maintaining overall precision through the coordinated sequence of processing steps
Data Source
AI summary
A method for classifying a document in real-time is disclosed. The method includes identifying one or more sections of the document likely to contain text based on a contrast between dark space and light space in an image of the document. Optical character recognition is performed within the identified sections of the document to identify a set of words within each identified section of the document. The sets of words are extracted from the identified sections of the document, and a subset of the sets of words is selected for classifying the document based on a preconfigured option. The document is then classified by inputting the selected subset of words into one or more machine learning models. The method includes transmitting the document and the determined classification of the document to an external server.