Partial Barcode Decoding via Visual Embedding Comparison
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current barcode decoding systems fail to accurately decode barcodes that are partially visible, obstructed, blurry, or imaged under extreme lighting conditions, leading to incomplete payload determination.
Innovation Solution
A method and system that capture image data, generate a visual embedding, identify and decode partial indicia to determine a partial payload, filter candidate visual embeddings based on the partial payload, compare them to the generated visual embedding, and assign correspondence scores to recommend the full payload or suggest capturing additional image data if the score is below a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional barcode decoding systems are used, then decoding is simple and fast, but decoding accuracy deteriorates when the barcode is partially visible or degraded
Solution Approach 1:
The patent introduces visual embeddings as an intermediary representation that bridges the gap between partial barcode images and complete payload identification. The visual embedding captures semantic information from the partial indicia, allowing the system to infer the full payload by comparing against a database of known embeddings, thus resolving the contradiction between maintaining simplicity and achieving reliable decoding in challenging conditions
Solution Approach 2:
The system creates a digital copy of the visual appearance of the barcode indicia through visual embeddings. This embedding copy can be stored, compared, and matched against database entries representing complete barcodes, enabling accurate decoding even when the original physical barcode is partially visible or degraded
2Reliability
If the system captures additional image data to improve decoding accuracy, then reliability improves, but time consumption increases
Solution Approach 1:
The system implements a feedback mechanism where the quality of the initial decoding attempt is evaluated, and based on this evaluation, the system determines whether additional image captures are necessary. The feedback loop compares the confidence level of the partial decoding against a threshold and only triggers additional captures when needed, optimizing the balance between reliability and time consumption
3Measurement precision
If visual embedding comparison is performed to determine full payload, then decoding accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing visual embeddings of complete barcodes in a database during system setup or offline processing. This preliminary action eliminates the need for time-consuming real-time processing during actual decoding operations, as the system only needs to compare the partial embedding against pre-computed references using efficient similarity metrics
Data Source
AI summary
Systems and methods for enhancing decoding of indicia are disclosed herein. An example implementation includes: generating a visual embedding from image data; identifying, in the image data, a partial indicia and decoding the partial indicia to determine at least a partial payload; responsive to the at least partial payload not being a full payload, determining one or more candidate visual embeddings corresponding to the partial payload; comparing the one or more candidate visual embeddings to the visual embedding generated from the image data and assigning a correspondence score for each of the one or more candidate visual embeddings based on the comparison; and determining a recommendation based on the correspondence score for each of the one or more candidate visual embeddings.