Barcode Decoding via Sub-Character Evidence Integration
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Solution Overview
Problem
Existing barcode reading technologies struggle to decode barcodes when only a portion of the barcode is captured, especially in applications where the barcode is in motion relative to the scanner, as they rely on complete images to accurately interpret the data.
Innovation Solution
A barcode reading system that utilizes multiple partial images by integrating sub-character barcode feature evidence from each image, allowing for the combination and stitching of data to decode the entire barcode, even if it is not captured in a single frame.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a barcode reader captures images of a moving barcode, then the reading speed increases, but the completeness of barcode capture decreases resulting in failed decoding
Solution Approach 1:
The patent divides the barcode reading process into multiple independent image captures, where each image may contain only a portion of the barcode. The system segments the complete barcode information into multiple partial observations across different time points, then reconstructs the full barcode by integrating these segments through probabilistic data association and evidence integration algorithms.
Solution Approach 2:
The system performs preliminary actions by capturing multiple images of the barcode at different positions before attempting full decoding. By pre-capturing multiple partial views of the moving barcode and storing the associated evidence, the system ensures that complete barcode information is accumulated before the final decoding step, even when individual frames are incomplete.
2Measurement precision
If a barcode reader requires complete barcode images for accurate decoding, then decoding accuracy improves, but the system cannot handle moving barcodes captured in multiple partial images
Solution Approach 1:
The patent introduces an intermediary evidence integration layer between image capture and decoding. Instead of directly decoding individual images, the system uses probabilistic data association and evidence integration algorithms as intermediaries to combine information from multiple partial images. This intermediary process reconstructs the complete barcode information while maintaining decoding accuracy, enabling the system to handle moving barcodes effectively.
Solution Approach 2:
The system changes the parameter of image completeness from a binary requirement (complete or incomplete) to a continuous evidence accumulation process. By transforming the decoding problem into one of integrating probabilistic evidence from multiple partial observations, the system maintains high decoding accuracy even when individual images are incomplete, thus adapting to moving barcode scenarios.
3Reliability
If multiple partial images are captured to ensure complete barcode coverage, then the likelihood of successful decoding increases, but the processing complexity increases
Solution Approach 1:
The patent extracts only the essential barcode feature evidence from each partial image, separating the critical decoding information from redundant data. By extracting and storing only the necessary sub-character barcode feature evidence (such as bar patterns, spacing, and positional information) from each image, the system reduces processing complexity while maintaining high decoding success rates through efficient evidence integration.
Data Source
AI summary
Systems and methods for decoding a barcode are disclosed. A scan signal for a first portion of the barcode is acquired. Integrated sub-character barcode feature evidence for the barcode is stored including evidence for a first plurality of possible sub-character barcode feature interpretations for a first plurality of sub-character barcode features. Sub-character barcode feature measurements are extracted for a second plurality of sub-character barcode features from the scan signal. Sub-character barcode feature evidence is determined for the second plurality of sub-character barcode features, including evidence for a second plurality of possible sub-character barcode feature interpretations for the second plurality of sub-character barcode features. Whether a portion of the integrated sub-character barcode feature evidence corresponds to a portion of the sub-character barcode feature evidence is determined. The sub-character barcode feature evidence is integrated into the integrated sub-character barcode. Character values are determined from the integrated sub-character barcode feature evidence.