AI Document Digitization System Accuracy
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Solution Overview
Problem
Current OCR technologies are inadequate for accurate data extraction from non-digital documents, particularly those with poor image quality, less common alphabets, and handwritten or cursive text, falling short of human-level accuracy.
Innovation Solution
An AI-based document digitization, transformation, and validation system employs multiple digitization services, selects results with the highest confidence scores, and harmonizes outputs from different platforms into a common format for optimal accuracy, using machine learning models for field extraction and error correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional OCR technology is used for data extraction, then the process is simple and fast, but the accuracy is insufficient especially for poor image quality, less common alphabets, and handwritten text
Solution Approach 1:
The patent combines multiple digitization services (including but not limited to OCR technologies from different providers) into a unified system. Each service processes the same document image independently, and their results are merged through a selection mechanism that chooses the most reliable output based on confidence scores and cross-validation, thereby improving accuracy while managing complexity through systematic integration
Solution Approach 2:
The system implements a feedback mechanism where the outputs from multiple digitization services are compared and validated against each other. Confidence scores from each service provide feedback on reliability, and the system uses this feedback to select the best result or request reprocessing if needed, creating a self-correcting loop that enhances accuracy
2Reliability
If multiple digitization services are employed to improve accuracy, then data extraction reliability increases, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by not always processing all digitization services for every document. Instead, it uses a tiered approach where simpler documents may be processed by a single service, while only documents with poor image quality or ambiguous content trigger multiple service processing, thereby reducing average processing time while maintaining high reliability when needed
Solution Approach 2:
The system performs preliminary analysis of document characteristics (image quality, text type, complexity) before selecting which digitization services to deploy. This preliminary action allows the system to pre-determine the appropriate level of processing, avoiding unnecessary computational overhead for documents that can be accurately processed by single-service OCR
3Measurement precision
If multiple digitization services are used, then accuracy improves, but the complexity of harmonizing different output formats increases
Solution Approach 1:
The patent implements a universal output schema that all digitization services must conform to, regardless of their native formats. This multi-functional interface layer translates various service-specific outputs (JSON, XML, proprietary formats) into a standardized structure, enabling the system to handle multiple services without increasing complexity in the core processing logic
Data Source
AI summary
An Artificial Intelligence (AI) based document digitization, transformation and validation system extracts fields from digital documents via different document digitization processes. A document packet with a plurality of documents is initially accessed and any non-digital documents in the document packet are digitized. The errors in the digitized documents are corrected and non-English documents are translated into English. Each of the documents is provided to a plurality of digitization services for the extraction of fields by a plurality of field extraction models. If a field has multiple field instances extracted by more than one digitization service, then a field instance with the highest confidence score is selected for inclusion into the consolidated results. The consolidated results produced in different JavaScript Object Notation (JSON) formats are converted into a common JSON format which may be further validated and provided to downstream processes.