Document Processing Platform Using Machine Learning Models
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Solution Overview
Problem
Current document processing techniques are expensive, time-consuming, and inefficient, leading to poor customer service, inaccuracies, and wastage of computing resources due to the manual and error-prone conversion of paper-based documents into digital formats.
Innovation Solution
A processing platform utilizing machine learning models, position-based extraction, and automated data labeling to convert image-based documents into digital formats by identifying fields, generating label data, and verifying character strings with confidence scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual document processing techniques are used to convert paper-based documents into digital formats, then processing can be performed with simple equipment, but processing time is excessive and accuracy is poor
Solution Approach 1:
The patent replaces manual mechanical document processing with an automated system combining OCR technology, machine learning models, and computer vision algorithms. The system automatically performs document scanning, text recognition, field extraction, and data validation, eliminating the need for manual handling and significantly reducing processing time while improving accuracy.
Solution Approach 2:
The document processing system performs self-service by automatically capturing images, recognizing text, extracting relevant fields, validating data against schemas, and generating digital outputs without human intervention. The system includes self-correction capabilities through confidence scoring and automated validation, allowing it to process documents independently and efficiently.
2Reliability
If manual document processing is performed, then equipment complexity is low, but processing accuracy and reliability are poor
Solution Approach 1:
The patent segments the document processing task into distinct modular components: image capture, OCR text recognition, field extraction using machine learning models, data validation against schemas, and digital output generation. Each module handles a specific aspect of processing, improving overall reliability through specialized processing while maintaining manageable system complexity through clear separation of functions.
Solution Approach 2:
The system introduces intermediary components including confidence scoring mechanisms, data validation schemas, and error correction modules that mediate between raw OCR output and final digital documents. These intermediaries verify and validate processed data, ensuring high accuracy and reliability while providing a structured approach to managing system complexity.
3Productivity
If automated processing systems are implemented, then processing efficiency improves, but computing resource consumption increases
Solution Approach 1:
The patent implements partial processing by applying machine learning models and validation only to extracted fields that require verification, rather than processing entire documents uniformly. The system uses confidence scoring to determine which fields need manual review versus automatic acceptance, optimizing computing resource usage by focusing intensive processing only where necessary while maintaining high processing efficiency for routine documents.
Data Source
Figure 1A
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Figure 1C
AI summary
A device may receive image data that includes an image of a document and lexicon data identifying a lexicon, and may perform an extraction technique on the image data to identify at least one field in the document. The device may utilize form segmentation to automatically generate label data identifying labels for the image data, and may process the image data, the label data, and data identifying the at least one field, with a first model, to identify visual features. The device may process the image data and the visual features, with a second model, to identify sequences of characters, and may process the image data and the sequences of characters, with a third model, to identify strings of characters. The device may compare the lexicon data and the strings of characters to generate verified strings of characters that may be utilized to generate a digitized document.