Confidence Score Generation for Scanned Label Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing OCR technologies are not always perfect and can introduce errors due to factors like image quality, font variations, or handwriting inconsistencies, making accurate extraction of information from scanned labels challenging in fields such as healthcare, pathology, logistics, and document management.
Innovation Solution
An apparatus and method for generating a confidence score associated with a scanned label, utilizing a processor and memory to receive a profile with a label and metadata, generate a scanned label, and determine a confidence score using a confidence machine learning model trained with feedback from previous iterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If OCR technology is used to extract information from scanned labels, then information extraction can be automated, but accuracy deteriorates due to image quality, font variations, or handwriting inconsistencies
Solution Approach 1:
The system implements feedback by comparing the scanned label against the original label image and metadata, then using this feedback to generate a confidence score that indicates the reliability of the extracted information, allowing users to assess and correct potential OCR errors
Solution Approach 2:
The confidence score acts as an intermediary between the OCR extraction process and the final data usage, providing a reliability metric that mediates between automated extraction and human verification needs
2Measurement precision
If a confidence machine learning model is trained iteratively with feedback, then accuracy of scanned labels improves, but device complexity and training time increase
Solution Approach 1:
The system uses feedback from comparing scanned labels against original labels and metadata to iteratively retrain the confidence machine learning model, progressively improving accuracy while managing complexity through structured feedback loops
Solution Approach 2:
The system performs preliminary actions by pre-processing the label image and extracting metadata before feeding data to the machine learning model, preparing the data in advance to reduce computational complexity during training
Data Source
AI summary
An apparatus for generating a confidence score associated with a scanned label is disclosed. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a profile comprising at least a label containing a plurality of metadata associated with the at least a label. The memory instructs the processor to generate a scanned label as a function of the at least a label, wherein generating a scanned label comprises scanning the at least a label using a text recognition module. The memory instructs the processor to determine a confidence score associated with the at least a label as a function of a comparison between the scanned label and a plurality of historical scanned labels. The memory instructs the processor to display the confidence score using a display device.