Instant OCR Exception Handling for Document Uploads
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Solution Overview
Problem
Current document upload processes lack effective mechanisms for handling exceptions during the upload process, leading to immediate termination and user retries, which inefficiencies accumulate in backend systems handling millions of requests.
Innovation Solution
Implementing a system that performs instant optical character recognition (OCR) and utilizes a machine learning model for risk assessment, enabling exception handling routines to address errors without terminating the upload process, including a secondary review process for verifying document validity and dynamically approving uploads based on risk scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current document upload processes automatically terminate upon detecting exceptions, then system reliability is maintained, but productivity deteriorates due to forced user retries and accumulated inefficiencies
Solution Approach 1:
The system performs instant OCR and risk assessment preliminarily during the upload process itself, rather than waiting for backend processing. This preliminary detection allows the system to identify exceptions early and apply appropriate handling routines before the upload is fully processed, maintaining reliability while preventing productivity loss from forced retries
Solution Approach 2:
An intermediary exception handling routine is introduced between the upload process and final termination. This intermediary layer evaluates detected exceptions, determines appropriate responses (such as allowing continuation with warnings or blocking with user notification), and manages the upload outcome accordingly, thereby maintaining system reliability while improving productivity
2Difficulty of detecting and measuring
If instant OCR and machine learning risk assessment are implemented, then exception detection capability is improved, but device complexity increases
Solution Approach 1:
The backend system performs multiple functions using existing infrastructure: it conducts instant OCR, executes machine learning risk assessment models, and manages exception handling routines all within the same upload processing workflow. This multi-functionality approach improves exception detection capability without proportionally increasing device complexity, as the same system components handle multiple tasks
3Productivity
If exception handling routines are implemented to prevent immediate termination, then productivity is improved, but device complexity increases due to additional processing steps
Solution Approach 1:
The exception handling routine applies partial action by selectively processing only those uploads that require intervention. The system performs instant OCR and risk assessment on all uploads, but applies complex exception handling routines only when exceptions are detected, allowing productivity improvement without excessive complexity increase across the entire system
Data Source
AI summary
Systems for detecting and resolving exceptions associated with document irregularities during a document upload process are disclosed. One example of an irregularity is a deviation from an expected standard format for a document type of the document. The system can receive, via a document upload application installed on user equipment, a document to be uploaded to a user account maintained by the system. An OCR component may be used to detect exceptions associated with the document and the system may include a component to handle any detected exceptions to prevent termination of the document upload process.