Scanned Document ACR Feedback for Visual Misinterpretation Resolution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Automated character recognition (ACR) algorithms in scanned images often introduce typographical errors due to visual misinterpretations, leading to discrepancies in interaction data that can erroneously flag interactions as unauthorized, requiring manual intervention.
Innovation Solution
A computing system uses a machine-learning model to identify and resolve discrepancies in interaction data caused by visual misinterpretations in scanned images by applying adjustments to the data or the image itself, improving the accuracy of ACR algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated character recognition algorithms are used to extract interaction data from scanned images, then processing speed and productivity are improved, but measurement precision deteriorates due to visual misinterpretations and typographical errors
Solution Approach 1:
The system uses a machine-learning model to provide feedback on the accuracy of extracted interaction data by comparing it against expected values. When discrepancies are detected, the model analyzes whether these are genuine errors or visual misinterpretations, creating a feedback loop that improves data accuracy without sacrificing processing speed.
Solution Approach 2:
A machine-learning model is introduced as an intermediary between the automated character recognition algorithm and the final data processing stage. This intermediary analyzes discrepancies and determines whether they result from visual misinterpretations or actual data errors, enabling selective correction while maintaining automated processing.
2Measurement precision
If manual review of interaction data is performed to ensure accuracy, then measurement precision is improved, but loss of time increases due to manual intervention requirements
Solution Approach 1:
The system enables self-service by using the machine-learning model to automatically detect and correct discrepancies in interaction data. The model autonomously determines whether discrepancies are visual misinterpretations or actual errors, eliminating the need for manual review while maintaining high data accuracy.
Solution Approach 2:
The machine-learning model provides automated feedback that identifies and corrects errors in interaction data without human intervention. This feedback mechanism continuously monitors data quality and self-corrects discrepancies, replacing manual review processes.
3Productivity
If automated character recognition algorithms process scanned images, then productivity is improved, but reliability deteriorates due to erroneous flagging of interactions as unauthorized
Solution Approach 1:
The machine-learning model serves as an intermediary that analyzes discrepancies in interaction data and determines whether they result from visual misinterpretations or actual unauthorized modifications. This prevents erroneous flagging of legitimate interactions while maintaining automated processing speeds.
Solution Approach 2:
The system implements feedback by using the machine-learning model to continuously monitor and validate interaction data, correcting misinterpretations before they lead to erroneous security flags. This feedback loop ensures reliable interaction validation while maintaining high productivity.
Data Source
AI summary
In some examples, a system can use machine learning to automatically detect and resolve a visual misinterpretation of a scanned image generated by an automated character recognition (ACR) algorithm. For example, the system can receive an image of a physical document used to initiate an interaction. The system can execute the ACR algorithm to analyze the image and extract interaction data from one or more text fields of the physical document. Subsequently, the system may identify a discrepancy in the interaction data by comparing the interaction data to one or more expected values. In response, the system can provide input to a machine-learning model that can determine that the discrepancy was caused by a visual misinterpretation of the image by the ACR algorithm. The system can then initiate the interaction based on updated interaction data generated by applying an adjustment to the interaction data to address the visual misinterpretation.