Pallet Tag Tracking and Cluster Voting for Accurate OCR
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Solution Overview
Problem
Existing systems for managing pallet tags in retail facilities face inefficiencies due to unreliable computer vision object detection, manual verification of missing or damaged tags, and duplication of effort in correcting erroneous tag exceptions, leading to resource waste and reduced productivity.
Innovation Solution
A system utilizing object tracking and cluster voting to identify pallets across multiple images, generate confidence scores for tag presence, and prioritize handling of missing tag exceptions based on these scores, ensuring accurate and efficient pallet tag management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computer vision object detection is used to automatically analyze pallet images, then productivity is improved, but measurement precision deteriorates due to unreliable detection results in sub-optimal image conditions
Solution Approach 1:
The patent introduces an intermediary verification mechanism where human users verify computer vision detection results. When image quality metrics indicate sub-optimal conditions (gray scale, poor lighting, damaged tags), the system flags these cases for manual verification rather than relying solely on automated detection, thus maintaining productivity while improving precision for problematic cases.
Solution Approach 2:
The system implements feedback loops where detection results are continuously evaluated against image quality metrics. When detection confidence is low or image quality is poor, the system requests re-imaging or manual verification. This feedback mechanism ensures that unreliable detections are corrected, improving overall measurement precision while maintaining high productivity through automated processing of clear, unambiguous cases.
2Measurement precision
If manual verification is performed for every pallet tag with sub-optimal image data, then measurement precision is improved, but loss of time increases significantly
Solution Approach 1:
Instead of performing manual verification for every pallet tag, the system applies partial action by only requiring manual verification for cases where image quality metrics indicate sub-optimal conditions. Clear, unambiguous images are processed automatically without human intervention, while only problematic cases are flagged for manual review. This selective approach maintains high precision for verified cases while minimizing time loss by avoiding unnecessary manual verification of already-clear images.
3Reliability
If all pallet tag exceptions are processed equally, then completeness of error correction is maintained, but productivity deteriorates due to inability to prioritize critical exceptions
Solution Approach 1:
The patent applies local quality by differentiating exception handling based on the specific characteristics of each exception case. Rather than treating all exceptions uniformly, the system assigns priority levels based on factors such as confidence scores, image quality metrics, and the nature of the detection issue. High-confidence, clear exceptions are processed quickly with minimal intervention, while low-confidence or ambiguous exceptions receive higher priority for thorough verification. This differentiated approach maintains completeness of correction while significantly improving productivity by avoiding unnecessary detailed processing of obvious cases.
Data Source
AI summary
Examples enable pallet tag tracking and cluster voting for more accurate pallet tag management using images of a selected pallet. A tag manager tracks a pallet through multiple images of the pallet to ensure the same pallet appears in every image. If the pallet tag is absent from all the images, a tag missing confidence score is generated that indicates the degree of confidence that the tag is missing from the pallet and not merely out of view. The score is used to prioritize handling of pallet tag missing exceptions. If the pallet tag is present in the images, optical character recognition (OCR) results for each tag image are aggregated into a tag cluster with a confidence score calculated for each result. A pallet tag identification (ID) number is predicted based on the result having the highest confidence score to ensure the pallet tag ID is complete and accurate.